{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "960ceacb-3c3f-484c-95b9-172bd009b1a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a generic code for drawing a state's Home Districts for neutral map estimation\n",
      "In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\n"
     ]
    }
   ],
   "source": [
    "#  THIS IS THE PRIMARY VERSION TO USE FOR ALL STATES  #See MO for orig 2/10/24.  This one from AZ 2-16-24\n",
    "print(\"This is a generic code for drawing a state's Home Districts for neutral map estimation\") #Jan'24\n",
    "print(\"In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\")\n",
    "import shapely\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "from shapely.ops import nearest_points, transform\n",
    "from shapely.affinity import translate, scale\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "from scipy.optimize import minimize, minimize_scalar\n",
    "import math\n",
    "import time\n",
    "import ast\n",
    "# from HDmethods import *   #I want to implement this, but my HDmethods require shapely functions\n",
    "# see https://stackoverflow.com/questions/66877728/proper-way-to-use-import-when-calling-external-function for a future workaround\n",
    "\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly,LW=1):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,lw=LW)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,lw=LW)         \n",
    "def plotCenter(t,geom,FONTSIZE=10):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t,ha='center',fontsize=FONTSIZE)\n",
    "    \n",
    "def r3(number):\n",
    "    result = round(number,3)\n",
    "    return result\n",
    "\n",
    "def r5(number):\n",
    "    result = round(number,5)\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21e9386d-7b2c-43fd-8449-95f56359a96a",
   "metadata": {},
   "outputs": [],
   "source": [
    "Florida to-do 3/4-24\n",
    "test if we can read in unit topology and HD poly shapes, determine HDpops on units\n",
    "Note that we may have to alter our wedge-building to be more ice-cream cone vs. triangular wedge\n",
    "Then, figure out how to record the vtdfrag - vtd relationships.  Best may be to record a full vtd-use list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ab4a3d5-f4e7-4aec-b81c-0e786cd35b57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def getWeightedAvgAndSD(LIST, WEIGHTS):\n",
    "    nWeights = len(WEIGHTS)\n",
    "    normWeights = [WEIGHTS[i] / np.sum(WEIGHTS) for i in range(nWeights) ]\n",
    "    AVG = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        AVG += normWeights[i] * value\n",
    "    sumVar = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        sumVar += normWeights[i] * (value - AVG)**2\n",
    "    SD = sumVar ** 0.5     #don't need to normalize again since weights were normalized\n",
    "    return AVG, SD\n",
    "\n",
    "def squish(shapeList, cutLINE, farLINE, newFarLINE):\n",
    "    \"\"\"\n",
    "    This method compresses a list of shapes lying between the cutLINE and the farLINE)\n",
    "    such that the Polygons now lie between the cutLINE and the newFarLINE, which is closer to the cutLINE\n",
    "    Used to compress state outcroppings into a more compact state representation.\n",
    "     (I tried doing this point by point (see #'s), but this overdistorted geometries.)\n",
    "      (#This was a manual alternative to shapely.affinity.scale and .translate operations)\n",
    "       So instead, we run this AFTER established untransformed map topology, and \n",
    "        we simply scale and translate each unit.  This loses true connectivity.\n",
    "        Scaling is all lengths scale by compression of distance\n",
    "    The cut, far, and newFar LineString segments are preferably parallel\n",
    "    The method returns the modified shapes of all pollys\n",
    "    \"\"\"\n",
    "    if cutLINE.intersects(farLINE) or cutLINE.intersects(newFarLINE):\n",
    "        print(\"cutLine,farLine, newFarLine were\",cutLINE, farLINE, newFarLINE)\n",
    "        raise Exception(\"ERROR: the proposed cutLine intersects the current or proposed far line\")\n",
    "    newPollys = list()\n",
    "    for polly in shapeList:\n",
    "        pollyCP = polly.centroid\n",
    "        cutPt =     nearest_points(cutLINE,   pollyCP)[0]\n",
    "        farPt =     nearest_points(farLINE,   pollyCP)[0]\n",
    "        newFarPt =  nearest_points(newFarLINE,pollyCP)[0]\n",
    "        curr_cutX, curr_cutY =            pollyCP.x - cutPt.x, pollyCP.y -  cutPt.y\n",
    "        currFar_CutX , currFar_CutY  =    farPt.x - cutPt.x, farPt.y   -  cutPt.y\n",
    "        new_currFarX, new_currFarY =      newFarPt.x - farPt.x, newFarPt.y - farPt.y\n",
    "        newFar_CutX,  newFar_CutY =       newFarPt.x - cutPt.x, newFarPt.y   -  cutPt.y\n",
    "        distanceRatio = newFarPt.distance(cutPt)/farPt.distance(cutPt) #scale area ^length^2\n",
    "        XOFF,YOFF = 0., 0.\n",
    "        XFACT, YFACT = 1., 1.  #default = no stretch\n",
    "        # basic equation: (new-cut)/(curr-cut) = (newFar-cut)/(currFar - cut)\n",
    "        #  or: (new-curr)  = (curr-cut)*(newFar-currFar)/(currFar - cut)   # -1 to both sides\n",
    "        if currFar_CutX != 0:\n",
    "            XOFF =  curr_cutX * new_currFarX / currFar_CutX\n",
    "            XFACT =              newFar_CutX / currFar_CutX\n",
    "        if currFar_CutY != 0:\n",
    "            YOFF =  curr_cutY * new_currFarY / currFar_CutY\n",
    "            YFACT =              newFar_CutY / currFar_CutY\n",
    "        newPolly = translate(polly,xoff=XOFF,yoff=YOFF)\n",
    "        newPolly = scale(newPolly, xfact=XFACT, yfact=YFACT, origin='centroid')\n",
    "        newPollys.append( newPolly )       \n",
    "    return newPollys\n",
    "\n",
    "def getHDcp(TRACTCP,TRACTPOP, TRACTLIST, SPLITTRACTNO = -777,SPLITTRACTUSE = 1.):  #population centerpoint of a Home District or county (cluster)\n",
    "    cpx, cpy, sumPop = 0.,0., 0.\n",
    "    for tt in TRACTLIST:\n",
    "        USE = 1.\n",
    "        if tt ==    SPLITTRACTNO:\n",
    "            USE =   SPLITTRACTUSE\n",
    "        sumPop += USE*TRACTPOP[tt]\n",
    "        cpx +=    USE*TRACTPOP[tt] * TRACTCP[tt].x\n",
    "        cpy +=    USE*TRACTPOP[tt] * TRACTCP[tt].y\n",
    "    HDCP_ = Point(cpx/sumPop, cpy/sumPop)\n",
    "    return HDCP_\n",
    "\n",
    "#  The below four methods are TO SNAP to HD SHAPES without any solving / iteration\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    The optional xScale parameter is the ratio of longitude to latitude lengths scales (<<1 for far from equator)\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def buildPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a 4-tri polygon from four HD wedges emanating from the centerpoint\n",
    "    Pt = [Point(0,0)]*12                      #xScale has same meaning as in buildWedge\n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW]  #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ]  \n",
    "        Pt[nW*2] =   Point(CP.x + RADII[nW]/XSCALE*math.cos(ccwAngle), CP.y + RADII[nW]*math.sin(ccwAngle) )\n",
    "        Pt[nW*2+1] = Point(CP.x + RADII[nW]/XSCALE*math.cos( cwAngle), CP.y + RADII[nW]*math.sin( cwAngle) )        \n",
    "    POLLY = Polygon([Pt[0],Pt[1],Pt[2],Pt[3],Pt[4],Pt[5],Pt[6],Pt[7] ])\n",
    "    return POLLY\n",
    "\n",
    "def buildArcPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a fuller polygon from four HD wedges emanating from the centerpoint\n",
    "    twoPi = 2. * 3.1415926   #xScale has same meaning as in buildWedge\n",
    "    POINTS = list()                   \n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW] % twoPi                 #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ] % twoPi \n",
    "        midAngle = [0.75*ccwAngle + 0.25*cwAngle , 0.25*ccwAngle + 0.75*cwAngle ]  #avoid s sampling cone center for wide angles\n",
    "        if abs (ccwAngle - cwAngle) > 3.1415926 :  #angles straddle angle=0\n",
    "            midAngle = [0.75*(ccwAngle - twoPi) + 0.25*cwAngle , 0.25*(ccwAngle -twoPi) + 0.75*cwAngle ]\n",
    "        angList = [ccwAngle] + midAngle + [cwAngle]\n",
    "        for ang in angList:\n",
    "            POINTS.append(Point(CP.x + RADII[nW]/XSCALE*math.cos(ang), CP.y + RADII[nW]*math.sin(ang) ) )\n",
    "                          \n",
    "    POLLY = Polygon(POINTS)\n",
    "    return POLLY\n",
    "\n",
    "def getPolyPop(POLLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points contained by a polygon\n",
    "    WPOP, WLIST = 0., list()\n",
    "    for tt in CANDIDATELIST:\n",
    "        if POLLY.contains(TRACTCP[tt]):\n",
    "            WPOP += TRACTPOP[tt]\n",
    "            WLIST.append(tt)\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonCPP(POLLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county in getPolyPop\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if POLLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonHCunits(POLLY, HC, UNITLIST, UNITCP, UNITPOP, ALLFUSEDCOUNTIES, AREAFRAC, COUNTYGEOM, \n",
    "                  NEIGHBORCOUNTYLIST, COUNTYUNITLIST):\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"unit counties\" are added as whole units if a sufficient area fraction is captured.  For non-unitCs, we probe each component vtd / tract\n",
    "    \"\"\"\n",
    "    UPOP, ULIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county before we called this method\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        #1/9/24 - NEED TO MODIFY THE ORDER BELOW TO AVOID CREATING HOLES AND ENCLAVES ########################\n",
    "        \n",
    "        for c in latestClist:\n",
    "            for cc in list( set(NEIGHBORCOUNTYLIST[c]).difference(set(ALLFUSEDCOUNTIES)) ):\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        if cc+0.5 in UNITLIST:\n",
    "                            if POLLY.intersection(COUNTYGEOM[cc]).area >=  AREAFRAC[cc] * COUNTYGEOM[cc].area :\n",
    "                                intersectedClist.append(cc)   #for unit counties, intersections only count if they capture sufficient area\n",
    "                                newClist.append(cc)\n",
    "                                UPOP += UNITPOP[UNITLIST.index(cc+0.5)]\n",
    "                                ULIST.append( UNITLIST.index(cc+0.5) )\n",
    "                        else:                           \n",
    "                            intersectedClist.append(cc)\n",
    "                            newClist.append(cc)\n",
    "                            if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                                unitsToAdd = COUNTYUNITLIST[cc]\n",
    "                                UPOP += np.sum(  [ UNITPOP[uu] for uu in unitsToAdd ]  )\n",
    "                                ULIST += unitsToAdd\n",
    "                            else:\n",
    "                                for uu in COUNTYUNITLIST[cc]:\n",
    "                                    if POLLY.contains(UNITCP[uu]):\n",
    "                                        UPOP += UNITPOP[uu]\n",
    "                                        ULIST.append(uu)\n",
    "        latestClist = newClist.copy()\n",
    "        \n",
    "    return UPOP, ULIST\n",
    "\n",
    "def clusterSwell(CP_, CCBgeom, CCBpop, allUnits, unitCP, unitPop, unitNbrs, TGTPOP):\n",
    "    \"\"\"\n",
    "    This method grows a Home District by \"swelling\" from a corner county cluster.  First, we add the full cluster,\n",
    "     then we add closest neighbor units to the home district centerpoint CP_ until we reach the TGTPOP\n",
    "     \"\"\"\n",
    "    #global borderUnits\n",
    "    hd_CCBdist = [CP_.distance(geo) for geo in CCBgeom]\n",
    "    CCBno = hd_CCBdist.index(np.min(hd_CCBdist))  #ID's the closest cluster to this HD center.  Should contain the HD, but rarely will not\n",
    "    unitNo = allUnits.index(CCBno+0.25)\n",
    "    newList, prevList, addedList, addedPop = [unitNo], [unitNo], [unitNo], unitPop[unitNo]  #seed with the cluster pop and unit\n",
    "    while addedPop < 0.99 * TGTPOP and len(newList) > 0:\n",
    "        trySet = set(list())\n",
    "        for U in addedList:\n",
    "            trySet = trySet.union(set(unitNbrs[U] ) )\n",
    "        tryList = list( trySet.difference(set(addedList)) )\n",
    "        tryDist = [ CP_.distance(unitCP[UU]) for UU in tryList ]\n",
    "        idx = np.argsort(tryDist)\n",
    "        idxNo, newList = 0, list()\n",
    "        haveNotAdded = True\n",
    "        while idxNo < len(tryList) and haveNotAdded:\n",
    "            UU = tryList[idx[idxNo]]\n",
    "            if unitPop[UU] + addedPop < 1.01 * TGTPOP and wontEnclave(UU, addedList, unitNbrs, borderUnits) :\n",
    "                newList.append(UU)\n",
    "                addedList.append(UU)\n",
    "                addedPop += unitPop[UU]\n",
    "                haveNotAdded = False\n",
    "            idxNo +=1\n",
    "        prevList = newList.copy()\n",
    "        \n",
    "    return addedPop, addedList\n",
    "            \n",
    "def getFakeHC(HDPOLLY, HC, CCBlist, countyGeom, MAP) :\n",
    "    \"\"\"\n",
    "    This method is for setting a fake home county for counties in corner clusters, so that county neighbors can be found budding from the \"home county\"\n",
    "    We pick the county in the cluster closest to the map center and intersecting the HDpoly.  This will be an active county on the cluster boundary   \n",
    "    \"\"\"\n",
    "    MAPcenter = MAP.centroid\n",
    "    for L in CCBlist:\n",
    "        if HC in L:\n",
    "            distList, cList = list(), list()\n",
    "            for C in L:\n",
    "                if HDPOLLY.intersects(countyGeom[C]):\n",
    "                    distList.append(countyGeom[C].distance(MAPcenter))\n",
    "                    cList.append(C)\n",
    "    fakeHC = cList[distList.index(np.min(distList)) ]\n",
    "    return fakeHC\n",
    "\n",
    "def getLongDist(CP1, CP2, xScale = 1.0): #distance between two points with EW distance scaled down by latitude\n",
    "    #LAT = MAP.centroid.y\n",
    "    #xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "    dist = ( xScale*xScale * (CP1.x - CP2.x)*(CP1.x - CP2.x)  +  (CP1.y - CP2.y)*(CP1.y - CP2.y) ) **0.5 \n",
    "    return dist\n",
    "\n",
    "# THESE ARE THE METHODS USED IN SOLVING HD SHAPES\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def getCountyWP(T, WEDGEPOLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points in a wedge excluding a point\n",
    "        WPOP, WLIST = 0., list()\n",
    "        for tt in CANDIDATELIST:\n",
    "            if tt != T and WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                WPOP += TRACTPOP[tt]\n",
    "                WLIST.append(tt)\n",
    "        return WPOP, WLIST\n",
    "\n",
    "def getNonCWP(WEDGEPOLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by an infinite polygonal wedge for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def isIncludedA(STARTa, ENDa, TESTa): #determines if a test angle is between a start and end angle (True)\n",
    "    \"\"\"\n",
    "    The start angle must be less than the end angle when placed on the [0, 2 pi] interval  (ccw convention)\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    STARTa, ENDa, TESTa = STARTa % (2.*pi), ENDa % (2.*pi), TESTa % (2.*pi)  #place on the 2pi interval\n",
    "    isIncludedA = False\n",
    "    if STARTa  > ENDa :  #included angle straddles east\n",
    "        if   TESTa  >= STARTa or TESTa < ENDa :  #near-east between the two\n",
    "            isIncludedA = True\n",
    "    else:\n",
    "        if TESTa >= STARTa and TESTa < ENDa :  #normal case\n",
    "            isIncludedA = True\n",
    "    return isIncludedA\n",
    "\n",
    "def getNonCWP_c(STARTANGL, ENDANGL, HC, TRACTCP,TRACTPOP,COUNTYGEOM,COUNTYPOP,COUNTYTRACTLIST,\n",
    "                                    NEIGHBORCOUNTYLIST, UUDIST, UUANGLE, WEDGEPOLY) :\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a polygonal wedge for counties contiguous with the Home County (no hop-overs),\n",
    "    EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    Unlike its getNonCWP vanilla parent, this one uses angles rather than infinite wedges for units (still wedges for counties)\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if isIncludedA(STARTANGL, ENDANGL, UUANGLE[tt]): #WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getExitAngle(CP,WEDGEPOLY, MAP, A0, A1):\n",
    "    \"\"\"\n",
    "    This code determines the orientation angle from a CenterPoint to the intersection of a wedge with an exterior boundary\n",
    "    If an intersection is not found, we just take the average angle of the wedge start/stop angles A0, A1 as this orientation angle\n",
    "    MAP must be a single polygon for this to work in the revised code (tho could run thru all polygons in MAP if a multipolygon)\n",
    "    \"\"\"\n",
    "    EXITANGLE = 0.5* (A0 + A1) #this will be the angle from the x-axis to the exit beeline\n",
    "    if WEDGEPOLY.intersects(MAP.exterior):\n",
    "        edgeLine = WEDGEPOLY.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "        closePoint = nearest_points(edgeLine,CP)[0]\n",
    "        dx = closePoint.x - CP.x       #this and below lines were indented in original code***\n",
    "        dy = closePoint.y - CP.y\n",
    "        EXITANGLE =  pi/2. * np.sign(dy)  #default in case dx=0\n",
    "        if (dx != 0. ):\n",
    "            EXITANGLE = math.atan(dy/dx) + random.uniform(-0.01,0.01) #add wiggle to avoid exact NESW orientation in gridded states\n",
    "            if dx < 0. :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                EXITANGLE = pi + EXITANGLE\n",
    "    return EXITANGLE # this reorients 0th wedge to face boundary's closest point\n",
    "\n",
    "def getNewAngles(mWP, tWP, ADP, LEVEL_L, MINADJRATIO, MAXANGLE, MAXANGLERATIO, nUNFILLEDWEDGES): \n",
    "    \"\"\"\n",
    "    This method classifies Home Districts based on how their post-reoriented equi-angle max wedge pops stack up vs targets,\n",
    "     then changes wedge angles in some situations to pick up more population along the boundary\n",
    "    If there is one wedge that will fall short of a quarter district pop, then we adjust angles if it is sufficiently short    \n",
    "    (Using \"HD2\" code, the opposite wedge's pop will be constrained as well.)\n",
    "    If there are two opposite-facing constrained wedges, we do not adjust angles\n",
    "    If there are two adjacent constrained wedges, we classify as a corner (no angle change) if the adjacent wedge is sufficiently constrained,\n",
    "      otherwise we treat as a single constrained wedge\n",
    "    If there are three constrained wedges, the angles are unchanged; we use the 4th wedge to pick up all pop\n",
    "    If all four wedges are constrained, there is an error and we raise a flag.\n",
    "    mWP is the quadlist of maxWedgePops we would get by extending each wedge to the MAP boundary\n",
    "    tWP is the quadlist of targetWedgePops\n",
    "    LEVEL_L is the non-dimensional distance to the boundary at which we no longer adjust wedge angles to drive more near-boundary pop\n",
    "    MAXANGLERATIO is the max ratio of the wide angle (facing the boundary) to the normal angle (e.g. 90deg for four wedges) ...\n",
    "    but this is truncated near-boundary to MAXANGLE (e.g. 1.8 pi/2 instead of increasing all the way to 1.9 pi/2)\n",
    "      NOTE THAT this code does not consider nearly-shorted wedges -- those are a separate method called later if all mWP's > tWP's\n",
    "    \"\"\"   \n",
    "    isChange = False   #default; we are NOT changing angles\n",
    "    printDebuggg = False\n",
    "    NWEDGES = len(mWP)\n",
    "    avgWedgeAngle = 2.*math.pi / NWEDGES\n",
    "    minW = mWP.index(np.min(mWP))  #index of the wedge with the lowest max wedge pop\n",
    "    oppW = int( int(minW + NWEDGES/2) % NWEDGES )  #and its opposing wedge\n",
    "    Lstar = ( mWP[minW] / (0.25*ADP) )**0.5  #shortest wedge's nondim'l distance to boundary\n",
    "    \n",
    "    case = \"keep same wedge angles\" #default; no unfillable wedges or all but one are unfillable\n",
    "    if nUNFILLEDWEDGES >= NWEDGES:\n",
    "        raise Exception(\"ERROR! ALL WEDGES ARE CONSTRAINED for tract\",t,\".  IMPOSSIBLE!!\")\n",
    "    if nUNFILLEDWEDGES == 1:\n",
    "        case = \"constrain opp wedge\"\n",
    "        if Lstar >= levelL :\n",
    "            case = \"keep same wedge angles\"  #not close enough to boundary to distort HD shape\n",
    "    if nUNFILLEDWEDGES == 0 or nUNFILLEDWEDGES == NWEDGES - 1:  #far from boundary or with only one wedge w/large pop\n",
    "        case = \"keep same wedge angles\"   \n",
    "    if nUNFILLEDWEDGES == 2:  #must determine if opposite or adjacent           \n",
    "        if mWP[oppW] < tWP[oppW]:  #shorted wedges oppose each other, so ...\n",
    "            case = \"keep same wedge angles\" #...the HD shape will naturally widen to pick up adjacent pop\n",
    "        else:  #we have 2 adjacent (non-opposing) shorted wedges... but how shorted?  ID the adjacent wedge\n",
    "            for nW in range(NWEDGES):\n",
    "                if mWP[nW] < tWP[nW] and nW != minW :\n",
    "                    adjW = nW  #this is the adjacent wedge\n",
    "                    adjRatio = mWP[adjW] / tWP[adjW]\n",
    "            if adjRatio < minAdjRatio:  #we're close to a corner (this adjacentWedge is significantly constrained)\n",
    "                case = \"keep same wedge angles\"\n",
    "                if printDebuggg :\n",
    "                    print(\"Not adjusting wedge angles for tract\",t,\"as 2nd-sparsest wedge\",adjW,\"has pop\",mWP[adjW] )\n",
    "            else:\n",
    "                case = \"constrain opp wedge\"  #we will set the angles and target pops as if the adj wedge were not constrained\n",
    "    \n",
    "    if case == \"keep same wedge angles\":\n",
    "        isChange = False\n",
    "        WEDGEANGLE = [avgWedgeAngle for w in range(NWEDGES) ]\n",
    "\n",
    "    if case == \"constrain opp wedge\":  #Here, we modify wedge angles.  MWP's will be recalc'd outside the method\n",
    "        isChange = True  \n",
    "        wideAngle = min(MAXANGLE, avgWedgeAngle*max(  1,( 1.+(MAXANGLERATIO-1.)*(LEVEL_L - Lstar)/(LEVEL_L - 0.) )  ) )\n",
    "        WEDGEANGLE = [(2.*pi - 2. * wideAngle)/ (NWEDGES - 2.) for w in range(NWEDGES) ]  \n",
    "        WEDGEANGLE[minW] = wideAngle\n",
    "        WEDGEANGLE[oppW] = wideAngle\n",
    "    \n",
    "    return isChange, WEDGEANGLE\n",
    "\n",
    "def rebalanceTWPs(MWP, TWP, BARREDLIST=list()):\n",
    "    \"\"\"\n",
    "    This method iteratively increases targetWedgePops to accommodate wedges that have maxWedgePop < targetWedgePop.\n",
    "    The wedgePop gap is distributed equally among wedges that have some capacity and are not BARRED (e.g. an opposite wedge in HD2 method)\n",
    "     (If this pushes some wedges over capacity, this will be fixed in a later run through loop inside this method)\n",
    "    We also flag which wedges \"will fill\" = have sufficient capacity after the rebalancing of the targets to not use the entire maxWedgePoly\n",
    "    \"\"\"\n",
    "    DEBUGrTWP = False\n",
    "    NWEDGES = len(MWP)\n",
    "    unorderedCapacity = [MWP[w] - TWP[w] for w in range(NWEDGES) ]\n",
    "    idx = np.argsort(unorderedCapacity)\n",
    "    #for nn in range(NWEDGES):\n",
    "    #    print(MWP[idx[nn]])\n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        for nn in range(NWEDGES):\n",
    "            print(\"barred list, orig MWP, TWP\",BARREDLIST, r3(MWP[nn]),r3(TWP[nn]) )\n",
    "    \n",
    "    for nn in range(NWEDGES):  #going from least to most maxWedgePop here ...\n",
    "        nW = idx[nn]\n",
    "        if MWP[nW] < TWP[nW]: #need to redistribute extra target to higher-capacity wedges ...\n",
    "            wedgePopGap = TWP[nW] - MWP[nW]\n",
    "            TWP[nW] = MWP[nW]  #max out this wedge\n",
    "            nReceivers = 0.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....\n",
    "                    nReceivers += 1.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....                    \n",
    "                    TWP[WW] += wedgePopGap / nReceivers  #... so give it equal share of the gap, even if this goes over; we'll correct in later loop\n",
    "                    \n",
    "    WILLFILL = [1]*NWEDGES\n",
    "    for nW in range(NWEDGES):\n",
    "        if MWP[nW] < 1.001* TWP[nW]:  #required pop nearly or truly requires the entire wedge\n",
    "            WILLFILL[nW] = 0 \n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        print(\"adjusted TWP's are\",TWP)\n",
    "    return TWP, WILLFILL\n",
    "\n",
    "def solveWedge(nontractTWP,t, hC, STARTANGLE, ENDANGLE, MAXD, TOLERPOPS, tractCP, tractPop,\n",
    "               countyTractList, countyGeom, countyPop, neighborCountyLIST,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    #### NO LONGER USED; SEE FASTER SOLVEWEDGEB BASED ON UNIT-UNIT DISTANCES  *********\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop (which excludes the home tractPop)\n",
    "    The wedge has a fixed starting and ending angle.  Its maximum diameter is angle-related to max diameter of the state map\n",
    "    It uses bisection, NOT scipy minimize to minimize the square error in the wedge pop vs. target\n",
    "    The method passes back the final wedge pop and list of tracts in the wedge\n",
    "    \"\"\"\n",
    "    chgLoopNo, maxLoopNo = 10., 22.  #when we will start relaxing the pop tolerance, and when we give up\n",
    "    includedAngl = ENDANGLE - STARTANGLE\n",
    "    guessedR = MAXD / math.cos(0.5*includedAngl)  #max possible wedge radius, accounting for possible wide angle\n",
    "    popTol = TOLERPOPS[0]  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    #                      We loosen toward half the MAX tractPop if not converging\n",
    "    \n",
    "    offsetPop = 88888888.  #to force a reduction the first time through the loop\n",
    "    dr = guessedR\n",
    "    loopNo = 0\n",
    "    while abs(offsetPop) > popTol and loopNo < maxLoopNo:\n",
    "        loopNo +=1\n",
    "        popTol = max(TOLERPOPS[0], TOLERPOPS[0] + (TOLERPOPS[1] - TOLERPOPS[0]) * (loopNo - chgLoopNo) / (maxLoopNo - chgLoopNo) )\n",
    "        dr = 0.5*dr\n",
    "        guessedR -= dr*np.sign(offsetPop)\n",
    "        \n",
    "        guessedPoly = buildWedge(tractCP[t],STARTANGLE, ENDANGLE, guessedR,XSCALE) \n",
    "        iCP, iClist =  getCountyWP(t,guessedPoly, tractCP, tractPop, countyTractList[hC])\n",
    "        nonCP, nonClist = getNonCWP(guessedPoly, hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyLIST)\n",
    "        offsetPop = iCP + nonCP - nontractTWP\n",
    "        #print(t,loopNo,r5(guessedR),int(iCP),int(nonCP),r3(offsetPop+nontractTWP),r3(nontractTWP),\"t,loop,R,iCP,nonCP,pop,target\")\n",
    "        if loopNo >= maxLoopNo-1:\n",
    "            print(\"WARNING. Looped\",loopNo,\"times for t,angles,R\",t,r3(STARTANGLE),r3(ENDANGLE),r5(guessedR),\n",
    "                  \"wedgePop offset, target are\",int(offsetPop), int(nontractTWP) )    \n",
    "    finalPop = iCP + nonCP\n",
    "    finalList = iClist + nonClist\n",
    "    return finalPop, finalList, guessedR, loopNo\n",
    "\n",
    "#above = bisection.  Below solveWedgeB uses static unit-unit distances\n",
    "# Also tried scipy minimize, which doesn't seem any faster\n",
    "\n",
    "def solveWedgeB(nontractTWP,T, HC, POLLY, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that intersect,\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2x2 array\n",
    "    \"\"\"\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    \n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList = 0, 0, list()\n",
    "    notFull = True\n",
    "    while notFull :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist    \n",
    "\n",
    "def solveWedgeC(nontractTWP,T, HC, POLLY, STARTANGL, ENDANGL, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST, UUANGLE):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that\n",
    "    fall within the angle range (in lieu of wedgeB's check for intersection),\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2D array\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    STARTANGL, ENDANGL = STARTANGL % (2.*pi), ENDANGL % (2.*pi)\n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList, latestDist = 0, 0, list(), 0.00001  #dummy value\n",
    "    notFull = True\n",
    "    while notFull and i < len(UUDIST) - 2 :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if isIncludedA(STARTANGL, ENDANGL, UUANGLE[TT]) : #POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist \n",
    "\n",
    "\n",
    "def getPartialTract(t, HDTRACTLIST, offsetPop, UUDIST, tractPop, neighborLIST):\n",
    "    \"\"\"\n",
    "    If offsetPop is >0, this method identifies the tract with centroid farthest from Home t that can jettison at least offsetPop ...\n",
    "    ... by slicing out part of this tract.\n",
    "    If offsetPop <0, we ID a tract CLOSEST to Home t among neighbors of in-HD tracts to pick up a partial\n",
    "    We return the tractID and its new partial use\n",
    "    We have updated this method to pass the uuDist slice for tract t instead of computing tract distances inside here\n",
    "    \"\"\"\n",
    "    debugGPT = False\n",
    "    NTRACTS = len(tractCP)\n",
    "    bigDist = 9999999.\n",
    "    qualifyingTractList = list()\n",
    "    isWholeTract = False\n",
    "    if offsetPop > 0:\n",
    "        homeDist = [0.]*NTRACTS  #default = 0 to not get picked\n",
    "        for TT in HDTRACTLIST:\n",
    "            if tractPop[TT] > offsetPop:\n",
    "                qualifyingTractList.append(TT)\n",
    "        for TT in qualifyingTractList:\n",
    "            homeDist[TT] = UUDIST[TT]\n",
    "        if len(qualifyingTractList) == 0:  #very rare case where all in-HD tracts are too small.  Jettison nearly the whole farthest one\n",
    "            isWholeTract = True\n",
    "            for TT in HDTRACTLIST:\n",
    "                if tractPop[TT] > 0.9*np.average(tractPop): #set arbitrary min qualifying pop\n",
    "                    homeDist[TT] = UUDIST[TT]\n",
    "        targetT = homeDist.index(np.max(homeDist))\n",
    "        partialUseFrac = max(0.0001,(tractPop[targetT] - offsetPop)/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(\"positive offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    else: #pick up a partial\n",
    "        homeDist = [bigDist]*NTRACTS\n",
    "        for TT in HDTRACTLIST:\n",
    "            for TTT in neighborList[TT]:\n",
    "                if TTT not in qualifyingTractList and TTT not in HDTRACTLIST and tractPop[TTT] > abs(offsetPop):\n",
    "                    qualifyingTractList.append(TTT)\n",
    "        for TTT in qualifyingTractList:\n",
    "            homeDist[TTT] = UUDIST[TTT]\n",
    "        if len(qualifyingTractList) == 0 :  #rare case where most nearby tracts are small.  Add nearly the whole biggest one\n",
    "            isWholeTract = True\n",
    "            maxPop = 0.\n",
    "            targetT = -999\n",
    "            for TT in HDTRACTLIST:\n",
    "                for TTT in neighborList[TT]:\n",
    "                    if TTT not in qualifyingTractList and TTT not in HDTRACTLIST : # we'll take just one, even if doesn't fill gap\n",
    "                        qualifyingTractList.append(TTT)\n",
    "            for TTT in qualifyingTractList:\n",
    "                if tractPop[TTT] > maxPop:\n",
    "                    targetT = TTT\n",
    "                    maxPop = tractPop[targetT]\n",
    "        else:\n",
    "            targetT = homeDist.index(np.min(homeDist))\n",
    "        partialUseFrac = min(0.9999, -1.* offsetPop/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(t,\"'s negative offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    \n",
    "    return targetT, partialUseFrac\n",
    "\n",
    "def getAdjoiners(UNITLIST, UNITNBRS): #returns all units that neighbor a UNITLIST but are not in the UNITLIST \n",
    "    allNbrs = list()\n",
    "    for U in UNITLIST:\n",
    "        allNbrs = allNbrs + UNITNBRS[U]\n",
    "    adjoiners = list( set(allNbrs).difference(set(UNITLIST)) )\n",
    "    return adjoiners\n",
    "\n",
    "def get2nbrs(ULIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    Get the combined list of first- and second-level neighbors of a LIST, using neighborList connectivity.  Excludes the source list\n",
    "    \"\"\"\n",
    "    firstList =  getAdjoiners(ULIST, UNITNBRS)\n",
    "    secondList = getAdjoiners(firstList, UNITNBRS)\n",
    "    twoLevelList = list( set(firstList + secondList).difference(set(ULIST) ) )\n",
    "    return twoLevelList\n",
    "\n",
    "def getContigFromStarter(starter, VLIST, NEIGHBORLIST):  #all contiguous units in VLIST, starting from starter unit\n",
    "    foundList, newList, prevList = [ starter ], [ starter ], [ starter ]\n",
    "    while len(newList) > 0:\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()        \n",
    "    return foundList   \n",
    "\n",
    "def isContiguous(VLIST,NEIGHBORLIST):\n",
    "    \"\"\"\n",
    "    This method determines if all items in a list form a continuous chain of neighbors based on the passed neighborlist\n",
    "    Empty lists are considered contiguous.  If discontiguous, a less-than-majority piece list is returned\n",
    "    \"\"\"    \n",
    "    isUnbroken, newList, foundList = True, list(), list()\n",
    "    if len(VLIST) > 0:\n",
    "        foundList, newList, prevList = [ VLIST[0] ], [ VLIST[0] ], [ VLIST[0] ]\n",
    "    while len(newList) > 0:\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()\n",
    "        \n",
    "    smallPieceList = foundList.copy()\n",
    "    if len(foundList) < len(VLIST):\n",
    "        isUnbroken  = False\n",
    "        if len(foundList) > 0.5*len(VLIST): #return a contiguous sub-list that is guaranteed to have at most half the total units in the list\n",
    "            smallPieceStarter = list( set(VLIST).difference(set(foundList) ) )[0]\n",
    "            smallPieceList = getContigFromStarter(smallPieceStarter, VLIST, NEIGHBORLIST)\n",
    "    return isUnbroken, smallPieceList\n",
    "\n",
    "def enclaveCheck(UNITLIST,UNITNBRS,maxLoops=4):\n",
    "    \"\"\"\n",
    "    This method determines if a list of units has an unbroken boundary AND whether its complement has an unbroken boundary\n",
    "    If the complement boundary is broken, there is an enclave or the district is so disconnected its adjoining units aren't contiguous\n",
    "    The method returns contiguity of boundary and of its adjoiners.  Also returns the lists of boundary and complement-boundary units\n",
    "      If either of these is broken, only a contiguous sublist is returned that is guaranteed to be smaller than half (for finding enclaves)\n",
    "      maxLoops is the number of loops for expanding neighbors-of-neighbors for contiguity of the units outside the passed unit-list\n",
    "    \"\"\"\n",
    "    UNITSET = set(UNITLIST)\n",
    "    ADJLIST =  get2nbrs(UNITLIST, UNITNBRS)  #in case direct adjoiners are only queen-adjacent\n",
    "    #adjoinersOfAdjoiners = getAdjoiners(ADJLIST,  UNITNBRS)\n",
    "    #BDRYLIST = list( set(UNITLIST).intersection(set(adjoinersOfAdjoiners)) )  #this might fail for queen-adjacent boundary units\n",
    "    BDRYLIST = list (set(get2nbrs(ADJLIST,UNITNBRS)).intersection(UNITSET) )  #in case inHD boundary is only queen-adjacent\n",
    "    noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)  \n",
    "    unbroken, smallPieceList = isContiguous(BDRYLIST, UNITNBRS)\n",
    "    if not unbroken: #could be that district's boundary intersects the state boundary or there is a nonHD enclave inside the HD\n",
    "        unbroken, smallPieceList = isContiguous(UNITLIST, UNITNBRS)  #slower check for full district, not just its boundary \n",
    "    nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "    while nLoops <= maxLoops and not noEnclave:\n",
    "        nLoops +=1\n",
    "        newSet = set(ADJLIST)\n",
    "        for UU in ADJLIST:\n",
    "            newSet = newSet.union( set(UNITNBRS[UU]).difference(UNITSET) )\n",
    "        ADJLIST = list(newSet)\n",
    "        noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)\n",
    "    #isJiggy = noEnclave and unbroken\n",
    "    return unbroken, noEnclave, smallPieceList, enclaveList\n",
    "\n",
    "def getBdryNonEdgers(UNITLIST, UNITNBRS): #all in-district boundary units that neighbor a non-district unit\n",
    "    ALLnonHDnbrs = set()\n",
    "    for UUU in UNITLIST:\n",
    "        ALLnonHDnbrs = ALLnonHDnbrs.union(set(UNITNBRS[UUU])).difference(set(UNITLIST))\n",
    "    BdryNonEdgerSet = set()\n",
    "    for UUU in UNITLIST:\n",
    "        if len(set(UNITNBRS[UUU]).intersection(ALLnonHDnbrs)) > 0:\n",
    "            BdryNonEdgerSet.add(UUU)\n",
    "    return list(BdryNonEdgerSet)\n",
    "\n",
    "def getEnclaveLists(UNITLIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    finds ALL units enclaved by a UNITLIST, parsed into lists of contiguous pieces\n",
    "    We assume the largest contiguous complement to the UNITLIST is not an enclave, but rather the majority of the HD complement\n",
    "    if the UNITLIST's complement (all couldBeEnclaved) is contiguous, the returned list will be blank\n",
    "    \"\"\"\n",
    "    eSets, nU = list(), len(UNITNBRS)\n",
    "    offmapList = list()\n",
    "    for i,L in enumerate(UNITNBRS):\n",
    "        if len(L) == 0:\n",
    "            offmapList.append(i)  #offmap list are units with no neighbors (were surrounded)\n",
    "    complementSet = set([i for i in range(nU)] ).difference( set(UNITLIST + offmapList) )    \n",
    "    remaining2nbrs = get2nbrs(UNITLIST, UNITNBRS)\n",
    "    \n",
    "    isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS) #quicker than contig check on entire complement\n",
    "    while not isContig:  #this will kick out before writing the final sublist = map majority\n",
    "        starter = shortList[0]\n",
    "        newEnclaveList = getContigFromStarter(starter, list(complementSet), UNITNBRS)\n",
    "        eSets.append(set(newEnclaveList))\n",
    "        remaining2nbrs = list(set(remaining2nbrs).difference(set(shortList)) )\n",
    "        isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS)\n",
    "        \n",
    "        # couldBeEnclaved = list( set(couldBeEnclaved).difference(set(newEnclaveList)) )  #revised 18-Feb-24 -- was slower\n",
    "    fused_eSets = set()\n",
    "    for i, eSet in enumerate(eSets):\n",
    "        fused_eSets = fused_eSets.union(eSet)\n",
    "        for j in range(i+1, len(eSets) ) :\n",
    "            eSets[j] = eSets[j].difference(eSet)  #in case contiguity occurs, but not in 2-neighbor list; avoid double-count\n",
    "    remnant_eSet = complementSet.difference(fused_eSets) \n",
    "    eLengths = [len(eSet) for eSet in eSets]\n",
    "    if len(eSets) > 0:\n",
    "        if len(remnant_eSet) < np.max(eLengths):  #exchange the small remnant for the biggest found \"enclave\" (usually the major complement) \n",
    "            eSets[eLengths.index(np.max(eLengths))] = remnant_eSet.copy()\n",
    "    eLists = [list(eSet) for eSet in eSets]\n",
    "    return eLists\n",
    "\n",
    "def wontEnclave(proposedU, dList, NBRLIST, mapBDRYLIST):\n",
    "    \"\"\"\n",
    "    This method checks if adding a proposedU to a dList (list of units in a district) will create an \"enclave\" of units in the dList's complement\n",
    "    via two problems:  A) the dList will now have a discontiguous set of units on the map boundary.  The full map boundary list is mapBDRYLIST\n",
    "      B) The non-dList neighbors of dList units aren't contiguous\n",
    "    The method doesn't require that the dList wontEnclave without the proposedU included; it just checks the proposedU + dList combination\n",
    "    It also doesn't check that the dList is itself contiguous with or without proposedU\n",
    "    In that sense, it is more limited than enclaveCheck\n",
    "    \"\"\"\n",
    "    wontEnclave = True\n",
    "    newList, newSet = dList + [proposedU], set(dList + [proposedU])\n",
    "    unitsOnBoundary = list( set(mapBDRYLIST).intersection(newSet) )\n",
    "    wontEnclave, __ = isContiguous(unitsOnBoundary,NBRLIST)  #first check - does district touch MAP boundary in multiple places? (quick FAIL)\n",
    "    if wontEnclave: #slower check below for internal enclaves\n",
    "        adjoiners = get2nbrs(newList, NBRLIST)  #2-level to protect for queen adjacency in boundary        \n",
    "        wontEnclave, __ = isContiguous(adjoiners,NBRLIST)\n",
    "        if not wontEnclave:\n",
    "            nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "            maxLoops, ADJLIST = 6, adjoiners #unlike enclaveCheck, here we just arbitrarily set a number of loops for search expansion\n",
    "            while nLoops <= maxLoops and not wontEnclave:\n",
    "                nLoops +=1\n",
    "                adjSet = set(ADJLIST)  #align nomenclature w enclaveCheck\n",
    "                for UU in ADJLIST:\n",
    "                    adjSet = adjSet.union( set(NBRLIST[UU]).difference(set(newList)) )\n",
    "                ADJLIST = list(adjSet)\n",
    "                wontEnclave, __ =   isContiguous(ADJLIST, NBRLIST)\n",
    "        \n",
    "    return wontEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d041d328-83e0-45db-a869-e0956c23ee26",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the state postal code; e.g. OH  FL\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, enter 1 below to use fl_pl2020_vtd.dbf as this state's population data file\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "  1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attempting to read in state unit geometries.  Stand by ...\n",
      "I read in the Census popn data. FL has 21538187 peeps and is divided into 7211 shapes.\n"
     ]
    }
   ],
   "source": [
    "STATE = str(input(\"Enter the state postal code; e.g. OH \")).upper()\n",
    "popFilename = STATE.lower()+\"_pl2020_vtd.dbf\"\n",
    "print(\"OK, enter 1 below to use\",popFilename,\"as this state's population data file\")\n",
    "enter1 = input(\" \")\n",
    "if int(enter1) != 1:\n",
    "    popFilename = input(\"OK, then enter the pop data file.  Typically a xx_pl2020_vtd.dbf\")\n",
    "print(\"attempting to read in state unit geometries.  Stand by ...\")\n",
    "tractPopFile = gpd.read_file(\"state_map_files/\"+popFilename)\n",
    "trueTractGeom = tractPopFile['geometry']\n",
    "nTracts = len(trueTractGeom)\n",
    "tractPop = tractPopFile['P0010001']\n",
    "statePop = np.sum(tractPop)\n",
    "censusGEOID20 = tractPopFile['GEOID20']\n",
    "print(\"I read in the Census popn data.\",STATE,\"has\",statePop,\"peeps and is divided into\",nTracts,\"shapes.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bfdea9d-b0ea-497a-a556-6b488a3e333e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the number of districts in the state.  My guess is 28 28\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 67 counties in FL .  I will assign them county Nos 0 - 66\n"
     ]
    }
   ],
   "source": [
    "tractGeom = trueTractGeom.copy()   #for some states, we will modify geom, so preserve original\n",
    "#tractNAME20 = tractPopFile['NAME20']\n",
    "tractPop = tractPopFile['P0010001']\n",
    "inputfileCountyNo = tractPopFile['COUNTYFP20']\n",
    "vtdGEOID20 =  tractPopFile['GEOID20'] \n",
    "nDistricts = int(round(statePop/760000,0))\n",
    "nCutDistricts = 0\n",
    "nDistricts = int(input(\"Enter the number of districts in the state.  My guess is \"+str(nDistricts)))\n",
    "tractArea = [tractGeom[t].area for t in range(nTracts)]\n",
    "trueTractCP =   [tractGeom[t].centroid for t in range(nTracts)]\n",
    "tractCP = trueTractCP.copy()  #for some states, we move secluded corners into the map, so save the true data\n",
    "tractCPx =  [tractCP[t].x for t in range(nTracts)]\n",
    "tractCPy =  [tractCP[t].y for t in range(nTracts)]\n",
    "inputCountyNumbers = list()\n",
    "for t in range(nTracts):\n",
    "    if inputfileCountyNo[t] not in inputCountyNumbers:\n",
    "        inputCountyNumbers.append(inputfileCountyNo[t])\n",
    "nCounties = len(inputCountyNumbers)\n",
    "print(\"There appear to be\",nCounties,\"counties in\",STATE,\".  I will assign them county Nos 0 -\",int(nCounties-1))\n",
    "sortedICNs = list(np.sort(inputCountyNumbers))\n",
    "countyNo = [sortedICNs.index(inputfileCountyNo[t]) for t in range(nTracts) ]\n",
    "\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adffc7a4-a78c-477f-8ea4-7f8e5109fa2a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now build the county-by-county geometries, hoping there are no islands\n",
      "Building county geom for county 0\n",
      "Building county geom for county 20\n",
      "Building county geom for county 40\n",
      "Building county geom for county 60\n",
      "Here is your original county-base map before any triage; e.g eliminating coastal unpopulated tracts w pop < 4.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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OxCHlWqZwX3jhBQRBsD4IKSjA/6DloyC6ms64Ph5K0pJTZB9JwL5F+efiVQYHzNpM0m+dogaPlNq2T58+1veNGjWibdu21K5dm6VLl/LEE08AMGjQICZNsuSkaNq0KXv37uXLL7+kc+fOhfoym82IooRKLDrfmpmZybBhw/j666+ZOXNm0fPV6crMt5Lv81GVyJQREXIHf0eNxRvwabqNZoIb4eHh1ApvzJy5V0l0bYw6px7Hsy6ScUPGRqXh3NlDzBy/n84Pt2bx558X9ZkRBNp12IspKwaTPpH9F8chGTOr/DxLxTsMgtpB5Dxw8IZGZYvWXbt28eGHH3L48GFiY2NZu3ZtIWtYZmYmb7zxBuvWrSMpKYmaNWsyYcIEXnrppWo8EYX/MrLRTHbULetnp9410Xjaoq1ROF7NvoU39i28ObD/AD//vI3GjRujcrP8vxnjsklZc4HU3y/zt+tF/oncg4+LFxl7bmAanoPavfSyDQr/G/xPWj4KYhvqhq6uCym/nidty9U8P4GyMWstN686nf6vwse0t7enUaNGXLhwAQ8PD9RqdZHaOPXr1y822sVk0gMU6/Mxbtw4+vXrV2LyrZ07d+Ll5UVISAijR48mISGhSJvSQkdv3LjB008/jbu7O3Z2dkUsECNHjiwS1dOmTZsKWz48zZaif2fjdjJv3jwCAgL4a+0enBq2pIP3edrU3ECgaRrmc4+TdfoRvn/3FVq3tmPiW69YhEqtWvTr1w8vr9v+FWp7H2y8WqBzDQOoNofdUmn9EmjsYPUo2P9VmSahrKwsmjRpwqJFi4rdPmnSJLZs2cLy5cs5c+YMkyZNYvz48axfv746Rq/wP0Dm/jj0p5MA8BzTGKcuAdiGeRTfNjOTp4c/zddff42rqys29d1wH9EA3+mtce4TzM1bcUx881U+af8aaknEnKwn7sND3HhnLxmR18k5m2ytLi5LMqZUPVK2EXOmAdlUDb5nCg8U/9OWj3zcHgslfUc0GTuvk7HzOho/e7xeDkcQS35ad5ceIknczq5/GiGY1XTucgKVpnx+I7m5uZw5c4aOHTui1Wpp2bKlNRtsPufPnycoKKjIvnq9JVeJwXig0PqVK1dy5MgRDh4sPpyzT58+PPbYYwQFBXHlyhXeeustunXrxuHDhwulty/J5yMlJYX27dvTtWtXNm/ejJeXF5cuXSpS/6V3796FolO0Wi0frN2AOjuLgzGx2KpFNIKIIAiIopBXBE5AEC1LSTIiYhmDp1M9Nmz4mF69evHYY48RGbkXHx83nny6K0+NGk2uJJFtNHLgYHeefmkY88Z+y5NR40v3mTFZSgFUl8NuqdTra3n9/hpsfo3IW0mca/I8tioRW1EssqzVqSsNu3bHNs9ZV7pDrPzzzz+MGDGCLl26ADBmzBi++uorDh06xKBBg+712Sn8B0j/w1KHyO+9doja0kssFHzYmTlzJoIgYFvfHQD7jn68/s5nTHnjNTqOfATht2nWjNJyrtnquAqgqeGA8UZhS6TKWYd9G19EncriPJ5fCluyPCBp/R2wqVM1dbkU7g+K+ABUTlpcB9XBpo4LScvOYLyZxY1pf2PX3JtjjteZ//mCIqbvxl0XcWzrON5YuIJt2zKB2zfwOx05X331VQYMGEBgYCAJCQnMnDmT9PR0RowYAcCUKVN4/PHH6dSpE127dmXLli1s3LiRnTt3FhmrpQIu2OjCrOuuX7/OK6+8wrZt20oMz3388cet7xs2bEiLFi0ICgpi06ZN1lolULL4yLdAFBQWNWvWLNKuuKkdtVqDOjuTTd+UXnxQq8uiWfgmNFrIxJlB/u25fPkyERERTJ48mWnTpnHgwAEmTpxIDZ+2PPPMM8TFxZGdlcPyL1Ywc+ZMPvjgA7Zs2cIjjzzCjh07ikxbSZLFcsT9sHzk0/cD/klOoenRz3nWpis5KlvKYxx67uRVJrsdswqUxLphfPjTL0S26IKrjw/pRw5w4uw5ak2axhvnY7ARBWxFERtRxFYl5C1F7PLEjZ1KRG2U8NNLeDnaImpFBK0KVEKVhlor/HvQ+jmQezkNc7Ie0ce+xHZlPezMmzcPtVbDpGn/Z3nQsFXj1COIGuPaYUrNJSMyBllvQlvTmZxTFkuLNtARx07+yJJM1sE4MiKvIxslEPIqX4sCCAKy3oTa2w6fSc2r4xIo3CMU8VEA2zAPaszpQM7xW2QfSyTn+C1izh6hLr6M/PQzHn3s9jy9qFIT3vcrXL6+SsuWu3nttRp06LAbsDztFyQmJoYnn3ySxMREPD09adOmDfv27bNaNh5++GG+/PJL5syZw4QJEwgNDWX16tV06NChyBjNZos40GhuV/49fPgwCQkJNG/evEA7M7t27WLRokXk5uYWKRTn6+tLUFAQFy5cKLS+pGmXDRs2FLBARFKjRg3Gjh3L6NGjC7XLn9pxcXGhc+fOzJo1iymPDiYq+jp6k0SOWUJCRpJkZFlGkvOWxgy89c8CkGIexaMPTQMsYqhFixbMnj0bgPDwcE6dOkVERATPPPOMVSyV12dGzpu2ElT3T3zEJt/EI+4QGTaunO3UBI3GllxJJkeyXJ/bS9n6uTfwXA0Pwmr7kZ23LmPWXNZNf40/+3ZCUKsRBJHmb89CX78xh9Ky0BfoTy/J6M0SBaXl0n+yCEu3rCuUOk0EQaNC0KoQtCKixrK0fM5bp1UhaETMapnFF7/BGG5LPc/62GnssFXbYqe2K/a9jcrmXyVs5syZw5o1azh79iy2tra0a9eOefPmERoaClhKALz55pv8/vvvXL58GWdnZ3r06MHcuXPx87v7Eg73mtzLlpIRau+ScyCV9bBz+PBhFi5cyJEjR4p814JGhcbTDrdHQ6zrHDvUsDq657e3a+xZ7LGNcVnc+vpEER8UhX8fivi4A0EQsGvihV0TL8xZRgb85UPXPTexk4rPz6Bz9EKjEajn/HCJzpwrV64s87jPPfcczz33XJntDMacvHHevnl2796dEydOFGr37LPPUq9ePV5//fViK9QmJSVx/fp1fH0L1z4pyfJRnAViwoQJ6HQ6nnnmGaD0qZ3O9euVeE63Ei9y9OhwyBtmvvAAi0gqzh9m9erVAKX6zNxZIRdAlgwACNWUIbYsMrJSkBZ3w8OcQ87w9Wi1lh95G5WAjUrEtRRN1MnNkcH+t3+UP/roIwynjxeq+Dt16lRmt2larN+PLMtWkZNtlpC37gcgs4ErNVv7IRkkZIMZ2WhGznt/e13e0mDGnGbCZLS8z83SMzyrH5PUH/Kr3eq8rColIyCUKVBs1bbYaeywU9sRJ3ni6dIMPzsX7POsNg4qFfYqEXuViINahZ0ooi5livRuiIyMZNy4cbRs2RKTycT06dPp2bMnp0+fxt7enuzsbI4cOcJbb71FkyZNSElJYeLEiQwcOJBDhw5Vy5juBaUJxLIedubNm0dCQgKBgYGFtv/f//0fCxYs4OrVqxU6nrWPTAMJX0ShdrPBuW9wme0VHmwU8VEKKnsNzv1rkbnnprUuzJ2Ioorjx3Lp9NhCPLzWWZ/2Czo7ViUpyRZP9KSkNOs6R0fHIiGf9vb2uLu707BhQzIzM5kxYwZDhgzB19eXq1evMm3aNDw8PHj44YcL7SdJUrE/BGVZIKD8UzsFuXIlkstXniNfH9UMKpzNtH379qX6w1TUZ0Y2WcSbqL4/uTailj1HW30C15/dQXBgk0r3U9GKvxEREURERFh/+MPCwhjr/whda7ch5OkGPPfcc+XKA3MnFy+fQbU4kRmt3qZBs+bozXpyTDlkG7PJNmVb3+eYcsg2ZRd6n2PMsbbJb5eYk1ho/3OubyOlpgFppY7DRhSwzxMlDirR+t5eLWIvikgnTuC1bw/NVNCqUT20Ts6I9naI9vao7O0R7e0RHRwsS3t7hLw/yPyK3fksWbIELy8vDh8+TKdOnXB2dmb79u2F2nz22We0atWK6OjoQjfgfxPmDEOJuY/Ketjx9fWlV69ehbb36tWL4cOH8+yzz1Z6TLmX05ANEh7PNUTlcH8eHhSqDkV8lIEgCNg0cEd/rmh6cLA87YfU3Y6Xj4iD6hXmLvi6WEfOqkKlttw8vbz8y2hZYB+VihMnTvDDDz+QmpqKr68vXbt25eeffy6SIKukPB9lWSBK2qe4qZ2CnDv/IxoNmE0d6NlzaZHtkyZNol27dsyePZuhQ4dy4MABFi9ezOLFi61tKuIzI5nzHU4r993IubkYT+wFRARRBFGFKd2EoFYhOtuCqLot3gRAsDiLppuN/HF2JY8k7mBe8PNMCyyah6UiVLTir7+/P3PnzqVOnToAfDZyJqPWTGPRhCWMETsCxTsLl0mexUEyWkRrvuXCzcatsqdWiHp/buLm+q0E/vU71/KEU6169Rk55XUad+tBllkiKT2NZTPf48jWzWSlpOBcw59GT4/E+7GnyDJLxOkNHPbyh4EWcdxv95+8umJuqccVbGysQkR0sEdlZ3l/zWixnElr13Hr+Im8NnkiJk+83Dp+HEEQcJAkJL0eQaf710w1eb7YmFtfHid21n7853Ystk1ZDzsA7u7uhbZrNBp8fHys01WVwRCdgcrNBpWTktb9v4AiPsog91o6+tNJiHbFX6rHH3+c8/9c43rOVwjmRaxevpT6zTqU+rR/Nxhy0wHw8iq9RknBG6+trS1bt24tV/8lWT7KskAUR0lTOwVRqy0x/zpdzWK3t2zZkrVr1zJ16lTee+89goODWbBgAcOGDbO2qYjPjCxbCucVnLaqCIZ1C9GdmVVoXVk95WoELoc54eGsYVc7d9qwll+OOvB4sxml7peZmcnFixetn69cuUJUVBRubm4EBgZWqOLvgAEDALgYm4bNwuO83mk0y46uQ2iQY21Tnjwwd+Li4oaeW6TFFA3brgo6O4v84uHBBwWE09KlS3l92BMcPXqUsLAwRk+fQvTfkaz7aQU1a9Zk27ZtjB07lonNGhaK+knNzOL/Nv7FzhZtmfviM7hq1EhZ2UhZWXe8Mgt9NmdlIWVmYc7M5N3t22nh7k7A2bOkHjpkaZOdbT1GriTxWnQ0/Rwcie/dh3gAtdoqUlT29gi2duiPH8erhx/u3WqD1gF0DqB1zFuW8VldfTdfXU1n6/vsYwnYNakeC65slkCsmGOzlG2scCZqhQcXRXyUQfbheAA8nm8E7xTfJqTta0g7jNxQfcf12NFlPu3fDWbJCIC6mqYNSnI4LcsCUZGpnYK4uTUmJeU3TObl/L3Hhg7tpxZp079/f/r371/quMvymbmSdAi9IRUp7SgAqXFRZKo90ZltcVZZUrgLahWIahBFENUIasv7G/HxTH37XTZv305OejIh7iJfffY9zeqHgCRhTs4CWUblqEaWTHkXEpBl4m7u5YLLMgDUmhFQozHR1z5Hrb9c6vkAHDp0iK5du1o/T548GYARI0bw/fffs3LlSqZOncqwYcNITk4mKCioxIq/ALt/OE7w6TTMkpnfzu4kR8qlffv21u3FOQuXNX246qfVfPT9HOIW3EL1nIawsDDefvtta2K9NWvW8NVXX3H48GGSkpI4evQoTZs2LfPcJUniteN/sj7dC5u2LvTt3ta6bdasWURERLBv3z7CwsLKHXLs4mDPYzU82GS25c8DR3mif09wLX+45rhx47igUvF3VBT+/rctj7IkIWXnkJuayhPPPotaJbJ40SIc4LaAycwXNNmkLLP8PWSeuIp7My0YMiA3EwyZlqUpp4QRFMDOHXSOeS/nAu8LvGwKrFfroEYLsHUps2u/d9ty851/SP7pXLnFR3FWxtRrN0k5HY2NgyOnDkWhj03j1JvrsDM4ohG1pKtTCH2zDyobi3yXZZnspFRs3ZyLWPQAS4mGasjArHB/UMRHKegvpZJ1IA7RSYvWr3Tv6npdp6Pb48nRhNlEX4vBSWdAkqTi/4nugvxpA3U1Pf2U5HBalgWiIlM7BWkWPopLl2py9doYUpI3A0XFx91yNn4nN04VrvfitjcCh+zik3cVJCVHpuNXmXQNVrP5ES1e9g6cywzEo1k7NLUt1qfSLB+xW74EwF94gbrtX0UURRbfWI19ngWmNLp06VJquvuKVPw9cCYefeQRQpaPxWA24ODgwNq1a61TaeXNA3Mn/v7+DH/mcbqlhnO9cQanY6MZNGiQ1SqRlZVF+/bteeyxx4pERhVHiiGHD84dZsOtLJIEbxBgiq/eut1sNrNq1SqysrJo29YiSDp06MCGDRt47rnn8PPzY+fOnZw/f56FCxcW6d9Op4VsSE1NL9d1y2f8+PFs2LCBXbt2FRIeAIIoIum0DBv/MtcS4vnr77+LTDvkc6GlJURe7QBBkZeKP5jZZBEi+WLEkAm5GZAeA+tfBo8QaPIE6NMt6/Nf6TchN39dumV73sMKAA2HwKPflXmuou72bUE2y5UqvHn517/RHpLRAGaSSdlkmbZ2xp0MXSoaoxYnkytnZ2wibO5gAE7MXIdblgdX5ERqTuqEg0/hayjaqTFnGVH4b6CIjxIwJeaQ+PUJsiQ9GR20JERFAYVN325uboWe9q/k1uH9N804OQv41/mO3dvW4anpT70u7yCqqkaE3LZ8VI/4KC3DaWkWiIpM7dxJ7drdOX+hDnb2F8nOTsTOrviMipUlK8cyJZDr/iSezo0Qc/Vc9dKRcO08jfa4Y2iqxdPbG1kyg2QCSbYsZTOzv11JjRoXiHh3CoJkBsmMf5NuqGuXPu0FcP3wCjK1x/AXXiC062vW9YKgQmWMYcXJby3TP6Ka5r4dCXEJKKW3uyNm302augWy+s+/8bSRWL16NSNGjCAyMpIGDRpUylkYLNM5Dz3Ug8QZh/C/YWTYnBcKWSWGDx8OUGyEQ0FOpsYyJmo/V82eSKIDyBpEcxLrmtWllXtTTpw4Qdu2bdHr9UWE06effsro0aPx9/dHrVYjiiLffPNNsdNuderWgWNX+NzOleLtQ4WRZZnx48ezdu1adu7cSXBw0SgLo9HI0KFDuXDhAjt27ChReABIOWZAoOaqNSUfVKW2WCjutFJkWfJhENwJOpYzs3J2MnwQDL5Nofe8cu1SUPBWVHhIksTFFTuxO2mR5Cm2ichmGTeDJUorq5GBmn06cW3uLhxEF5xxJ+aN3aQ4JOKQ4YBB0OOEK1cX/03dsd3Qedx+cNF42pGZfAPJYC4zAZrCg48iPkog7iNLmFx0CyM9O7exri9o+o6IiCjmab87q1+fip3+CglxW4kVliP/ZaZBj/erxOlMMlvEh6aaKqNWh7WmPGi1Ft8Gs9lQ5X3LWKwMNb2608D39jTGcfkgubv1GBt5oi0hFHjT5Dn06tWP4cu2FchvomV07fqlHtNkyOFS4gc4G9oT0n9KoW22diG4p+5BTJhtXXc9FnZr2xPg9yg9aw2s7KkWi8Eo0epcJqg09OnQDIAWLVpw8OBBFi5cyFdfFU3+VpazcGZmOscXbkNntNxkvHEjR9bz008/FbJKlIcn929lR7Y3UBNn+QbeQjx/dOmPtkDxxNDQUKKiokhNTS0inD799FP27dtXKOR47Nix+Pr6Fon68XNz5ulr51keFMLOfw7SpW3LUsc2btw4VqxYwfr163F0dCQuzpIRxdnZGVtbW0wmE48++ihHjhzht99+w2w2W9u4ubkVcdqVTJbfAMPJA2iCS/8bKkJ+bhpz2VYzK2od2HuBoy84FJ87405kg8X6KdhU7PYgyzIJh85ZhUcisTR9Z6h1u9loQqWx9FnvgwHcOnWZzOVX0MhaXDM9QITc1iJZSRk4nHcg7sNDZNbMofYjHdC62KMNdgLZMhXu0Pbfl0NFoTCK+CiD9qEtSjV9l/y0H4oPvTm/w4/rqi+RtufQqOfHdz0eKS9PhUpVfdMu98MzPyfHAVvbTPbs6UH79jtwdCx/kb+yyM8KKwiFn5bMRsuPuFpb8sRJefKbFEdO4g3Mmgx8PR4pcj2fbjYNmIYsS8iykT/OLSExORKP3OPciNVAFYuPhLf2FLtelmVyc3OL3VaWs/Ct+DgCM7w55xvD5bQbTFjwJgajEUdHx0JWibI4lnKTHdneuJiv0dfdhvnh/Yptp9VqrQ6nBYXTggULKhRyDNAt2J/lEuy6eYsuZYwvIiICwOpPks+SJUsYOXIkMTExbNiwAaCIL8uOHTuK7Of3XCdufreLG2/OoW7/Zyr2v5aflVcuufhjscUIH3oP1r0If74H3d6y+E6UgqC2PHzYNS2fWAHISUjj+id7sJHs2R29n+WJf3Ls1Ali5z1uzQqdLzzy8QyrxZvJ81i8eDHTho/jpWefp3bXpgCkRcdybcV+3K65k/jJMQDMsgkRFYkbzqIKc8TWqeTpXIUHn//5wnIlUWOmxREv+adzZbQsnZCuU7DNqUuG4WRVDAtJMiLLAoJQPV+dLMv3xfIRGmIxCWu0uWRk3KjSvvMjXMQ7UqpLRosoETUla3BJkmjWrBmzZ88mPDycF154gdGjR1tvSiVx48wvANh6lvyEJggioqijZ/0Xear9T6SqQ4DifW4qi2SWiLURmBu5mB8dr3L16lVOnDjB9OnT2blzJ8OGDSMzM5NXX32Vf/75h6tXr7Jz504GDBhQurNwXgFGry51GfHGBE6cPMn+/ft56aWXGDFiBKdPny7X+EIcPRCkLOwFA/PDe5W9Qx75wqmiIccAgua2g2N5jlPca+TIkYClxEBJbe4UHgDOUyw+QOZcgZuPd0DKSCr3OVstH1LJ4qPYYoRNn4SeM2H3x7D307KPk3fdTMn6MhpauHXqMlc/3oXarCEjMBO7QcE0b92yxIKI+axbt479+/fj5+eHZ7M6+OcJDwDnQF8avzEY9eNeZLUyk1Y/k7SAdJLcb3Em+R/O7yuaQFDh34Vi+SgBQS1i19ST7KhbpG29im1DD9SuOkS7ioVopsedI8f2ArW1b1fJuCTJgCxXnziQJKnYjKjVSVz8aaKvjwPA1fUN/PyaVWn/xjwnXc0duT0kk2X+Xa0t+d+gMvlNAFKy94A93Dq3A7fAVuUapyyIBObu4vvdw7CIEAkf38fpXafskG3JLHHySgrXDt7ENsOIaJIJibaEgPoCp8yp/Lb4Ld5+LxZnZ2caN27Mli1beOihh8jJyamws3D0pYvUxgmVSlWiVaK46Zw7sVVrCRBvkWwuucz6tGnT6NOnDwEBAWRkZLBy5Up27tzJli1bcHJyqlDIMUBAQA24mECEew34cTXvDBtS5jirDEGg5qJ3ufryO6QfTyazXTtCDh9D0JZjGlXM+78sxfLRp08fa6RRIRo/AdvehKzikyUWJPU3SySWQ+uyw64v/ByJ+rAJFSJ2QwOp1SKUMOBhHit1vxs3bvDyyy+zdetWq8WqOHzCQ/EJL5wbZP9LI3BK+Xcmb1O4jSI+SsHl4TogCmTsvkHGjusAeIxuhE1tl3L3cfPEagS01GhWNTk/zJKxWsWHLMvVKj4kycyOHbMxGH9BpTJiNtsiinrUatBohnI2cxMn/1wCCHnmYdH6XijwPn+9IIjI5FuC8toIAqm5adzIvEktlzqoTMkEiqBTF77ByUYJUBUxBxekMvlNMmIvkm1/FoBb+q2E8nq5ro271yCuJ4h556fBxXCM2IRtUIr4+GPNabyikjFoBHyyJPJTeyXYi9x0VuOXZuJgTz+2z16PugSLVkWdhbcvW0XoKS+uesbTJrSob0dp0znFkSurMQolFzGLj49n+PDhxMYWFU5AhUOOGwb4cdrRngZHr7Dc1bdQBL0sy5zcu4Go6P2sTP+LzpoGDGo5EjtBi1/dcM6djCQgqCGOHpX3ObDtMZR6Jx/hbMNGSEaR+NF98Vn6V9k75k+XlGL5KJH8qBfn0pMTSrlmsvZbfFZsGpTsOGvMzuXcgq24pLuS4pBOyCs90TmX/B0WOoYkMXz4cKZMmUJYWFjZO9yBPiuT8/v20KBzd1x9FN+PfyuK+CgFQavCuVdNbOq6krLmArJRQqpAqJdJn8mt3N9x0bRFY1c185OyZECWq08clBbtUhXs2vUqCBvQasEsuaHVJmMweqN26EN01h94SzHcwiN/MIAMsoRA3nso8F62vhdkCnwGN8mAuy1ojJdQiSJpuOLpUFgwyCaL+FCXksmzPBlWCxJ3chunEl5iw4Z0Nm5I51Z8LILKqUj+i5Ku8QcffMCUKRYH1R8iByHoz/LDoXdoFDCIcO9mmM0S5y8mkXg1jZTr6TS7mAXAVZ1IdP9AQht44u5mR8FbTPlz4ZZNYnwc9U/5cC40jq7PDOHNt94s0SoBkJycTHR0NDdv3gSwCjkfHx98fHz46coh4gV/1FJqiX973377baljqkjIcT5uLpZkWhm2dsz99TeeaduMLbsW8onhdwAEWUa2EVjGQZYdyavcejhv55MwIDGAXnX60mnguEr9vwhqNbV/XcqlR0eQdvgG3jmZCLblLJYmV2Jq7voBy9K79Jt91v5YABza+5V4XqnnY4j77igOkgOZYXrChg+q0FTtvHnzUKvVTJgwodz7FGTgpKn8teQrVrz5Kk/Pno+zV8US4yk8GCjiowRkWSZ1zUWyDlqeAjT+Djh1D8S2fslPA/nk5tziytEvictcjdkmm7C6n1TZuCTJVO3TLtXl82E05mKWNlg/q0RL7L9WE48p83s8ZDB6DuepxjOq5fh3YhEfoFKX/G9QngyrBUlLtCQx8/RQsWjR2kJZOQvmv4iNjS203+bNmxk1ahRDhtyeAhAcwrFL34Zj+i9EXYolO+4NkrbE0ixFwhEIBC7Wc6LT8Eb4V1Eod1kYjRbx7VzfB1EllmmV2LBhQ6F6Hk888QQAk6a9zqvTX2fSVTUOUjwrm9a7547OryXf4AO3Gixw9+e7fYvR5QmP/9P049FBr6OWBJaseYssO5GGbmFcuXGKy1nXMKlF9tpcZGPqV9SZ+yULnviRwJqNyzX+Ox1Clz87mGZ7z5L+y7c4j3ilfEnZKpNoyysvsiatdH+qtN+vAGDXvHiH76sb9yPvzkBAQPeoDzVblc+xOJ/SKt6Wl+DwFjxVN5TvJozm5I7ttH98eKX6Ubi/KOKjBPTnUsg6GIdDpxrYNnBHG+iEUEblzIzU81yJWkSiaStIKtyzexLc+EWcgooP4zTmZpAWe5z0hONkpJ9Gb7iJUU7GqEnA0diMFv2WFdlHlo3VbvmoLvGhz72duTHdpikenr0JdGvO2Zu/YW/jQwv/QTjbVl2US5mYJEyYUZcy7QLly7Caj71jLUiDtu3s6d6tr3X9nVk570xjvn79erp27UqtWrWs64Y3nwHM4Ktdz1BH/yd6/Z8EOT/KlU7jCG3giZOthqBS/FWqhfwbX96NoyyrxMiRI63OmQAGs4FmkX+wQvBjxf5rACxr2pgW7iWn4K8uJg/px2ijgR6/zSE70+LDs2vQNlxdbo/lpRGfFbuvLMvMiBjKGr+z9N/1NOyyrD8y7DAadcmWtHyH0GeffZYhQ4aQ/vcREO3Q1a5l3V52UrZKWD48Q8HBB9aOAUcfqNW54MlYv0+nXkGkb72Gyrmwf5TZaOLsZ1twTnAmWZdG7Ve6Y+/hUuFh7N69u8IVb4tDa2OLIIpKwtN/MYr4KIHMvTdRudng1DUQ0bbsyyTLMocOPoJg0uFjfJqg8FHYB/hhNujJuHoJfUocBvkWGWmnydKfJZuLGHSW1O1IKnTZ/thQA1ttILfUG8iSio+ykWQj1trz1UR1+XwYJYlDBwfi17Iew9u9YF0f5F61DqblRTZKmAQTaqHq/g3Opb0BQKD0snVdcVk5CxIfH8+mTZuKVJXNp47pdqis56PjaOJx/5ztJGvYcuWeWr+8eJBEwTJPHyid5yEvX9q6N62q4VWYFZf3kJ3+K/a6Jnze/FWr8Lhx4wavv/46mzdvJicnh5CQEL799ttCZeSFhDBufbGTpJRktLVt8HvGj/ZLWhCQa8+Pz25Fp7OzpOgvQL5DqJSZCoApy4zbgNrYdLDU3ilXUrbK3nEf/Q6+7ws/DIRaXeHyjsLb7b1IT7JkQDWn5aKytzjXZ964RfTne3E0u5AWlE7DFwYjVvI3Yvjw4UXCnytT8Xb3iiXoMzMJbVd88TuFBx9FfBRAlmTM6QbMabkYb2ZiF+5VLuEBlh9jWTAhawyYSOX6uaWknTpApuYEeQ4JAIhGe2xNwTir2uGoqo+TZyOc/Bugcbo933tqmy1J0o7iDpNn+ai+r61aLR+SnpwcZ7Rql2rpv6LIsoxZkFCJVSO2ki7tB0CXEUhQ15GlZuUsyNKlS3F0dCySSTQr7hr7Tnezfm7ffA82zvd3flu+w/JRUWINlrDnTrqr/NJuaBmtq5dVV/azaJ/F7+Dd9uNpHtAUgJSUFNq3b0/Xrl3ZvHkzXl5eXLp0CRcXF+u+H3zwAfPnz+f7778nJCSE9959l20fbkYzJ5Dzjpm0/LU9XikyZhV8XHsM4Z1HIdjZYb52Fik7g1vvWZyQHRv54rVgVQVHXvK1L7UYYc328NQqWPGYRXho7C0+IDEHwM4DshIAIxonk7WcRMyOKPSb49Ggg96OhHXrXMKRyzmGwMAqqXh78dB+Qtt1xDOwZrn3UXiwUMRHHrIkc2vxcQxXLTUfBI2IQ+uKmYL9Nc+TmnmAdOEgRjkJ29y6BKknoFP5obXzwKF2LWxd/MtOtS4IJf6+yLKB6vzaqlN85ORNu+g0D0ZJbNksISEgVlHOlLSbFn8Pr4CeaJ1cCQ21LzErZ0G+++47hg0bho1N4XDL4/teBidQ57rQ4aF9qNSVq8RbpUh5Tr+VvGROeQURd+XWZO+taNp53j8rzpFkS22VXvUm0NP/djj0vHnzCAgIKOTEWrNmTet7WZZZsGAB06dPtwrGZcuX4+3tzXh5PKnZf7M38zi5akhwEvhpy1fYTY5A1EhIxsIXzvHRZxFsSg4zLpZSdF9ZxQgJ6QnvpJYoHsWp67H1NSCZJc4u3orDVTv0qmwCx7bDKaB8U6JljqEK0OpssHEop4OuwgOJIj7ykI0ShmvphT5nH7+FU7fy/ziGdH61ikYjkB/ZcSeybIRqtnxU17RLtt6Sd0KnvXvxMWfOHNasWcPZs2extbWlXbt2zJs3r0JPT7IsIwlVN2l8xfghANdTv6Gu/Ea58l/s3r2bc+fO8fPPPxcZm0NqExxuhuOY1pJbZ49bzO35AUDcfp+3Q97S8t5qmc+LArqzrZRtsUAIBSx7ok6F96RmhQqL3YksywhYgporypx9u9B9tYKPky25JpJVIpsK/q3LdyzBGr1053kKBU5QXzeUh+eXr25JPjFZKWw8/TkC8EL9/oWmkTZs2ECvXr147LHHCqTUH2v1wbhy5QpxcXH07NnTuo9Op6Nz587s27ePpUt/tK5//Mv6CC4SjnXt0MflYuOrRbS3w/XpkfDoC6gqFbJb8rUvqxihZffSvjsNRkMup99dj4vBgxSvFBqMH4CqAr5F5RpDAcrr51EQOxdX0m8lVHg/hQcHRXzkkbnnRpH7va6W8/0ZTCniA9lIdX9t1Wb5MFgsHzblSahUBpGRkYwbN46WLVtiMpmYPn06PXv25PTp09jbly/fgCzJSELVZBTNj5wBaFbvp2J9IorLf5HvR9CkSZNC66/9uBrXs70wazJRyU6YsvSWFCdQ+N6Tf5w71wkFN+d9FvK33W4sai3ftWyWMafmkvzLeXRBTqARb+8vCJgzDBarR05meS+JldzsTCI/nMzgn3YDcLxtO3QqLaaCJ5M/pAJjkwufRDFtBOr9tQ391StAxcTHawcWI0jpDA2fQ12nwhbOslLq59du8fYubAnw9vbm2rVrhdapdLbY13PH/73txYzihWLW3V8k2ZGcK47YSXoMHdQ0Gli1qf6rCt+69YjatqnaUwMoVB+K+Mgj5/gttAGOOHTwQxvohMpFd9/+qEs7rowRodQi7ndHdVo+9AZLumabKiiKl59HIp8lS5bg5eXF4cOH6dSpU7n6kCXp9g3uLrl+5CcA3HN74+rXqtSsnPmkp6ezatUqPv64cM0fY3YG6pOWG5vHhAbYe5ac0Kw0bty4wWtlOE3mM2b0GL7+5mve6fYyz7cs2Rcj/2qpckpsYuXyvm1cmvMu/ueSCQCSnUTspk/m8UGjKnU+xbF+7seEfP8Nhzt0IrpTF+t6KdfALa0NFxs3LaTjBVkmLfkMJ5x+ws6xBW81LhrFJEkSLVq0YPZsS+G/8PBwTp06RURERKF6Pnf+nxZ7IxTyrFT/EvTmNDSiPS7P18MtpPqqLJeX3LQssmITLZaUfIueLONi44U6R0XshXP4hRQfTajwYKOIjwJoAxyxa+J1v4eBxdhc9AfLaExDELIxGjUcPnyYrKwssrKy0Ov1GAwGDAaDtc6FSqWyvnQ6HW5ubnh4eODi4mJNmS1JktU8amNjg05nmQ6pLvGRb/mw1VVwjrscpKWlAZZKouXFYvm4+xtD+vVzXMi0pM+vE24pdV5W/guwZOaUZZknn3yyUH8qnS1G71g08b6kfBxNziNxeLRqXaExlcdpMp9169Zx4OAB/Hz9cOhQA4+nG9625ORN9ZjSDMhmmZTcZFR/pCLZl24d2zftJZzX7MRbBZdGdcO/WUfad3+iQudQHtwbNwLALvEWznv3WtWRb+wNMmzt+L5ZO6zqQ7BM4mQ7WYRiD8/i096XlVI/P0w6Li6uUOG9hISEItaQO22YZTljlpWU7Xav1cOO2K+o37gmHUKKDzG+11ya8wdOFP2fdsaGfgFjSN11RREf/1IU8fEAIcuWaBtZb0bSZnPh4hxycq6jz4khR38dkykdnQ6Sk+uyceNG7OzssLe3R6fTodPp0Gq12NvbI0kSZrMZs9mM0WgkPT2ds2fPoteXXihKnRcWeOPGDdavX4+rqyu+vr4EBAQUcYasDLkGy5SDndburvsqiCzLTJ48mQ4dOtCwYcNy7yfJEvJdOJtKksTVvd9yxTAXgEb+i3HwseRrKCv/BcCYMWMYM2ZMkfWiSk3wpKFkXrtCakQM+jUGDKEpaJ1dyz22spwm87mzxoba1QabuiUfJ+lCJiooNeeNIScL5zU7AbDZvIL+geHlHndF6dC3J/Q9A0DBAvXXRo6E/QfY91jPIvs89LmaOAcTA9XFP2iUlVI/ODgYHx8ftm/fTni45dwMBgORkZHMm1d0+qfgg0RZzpglJWV75513GD16NK+vyWHzxyvJGflLIUuW0WjkzTff5Pfff+fy5cs4OzvTo0cP5s6di59f+f1KJAxUUfBXlSBKIgliDJ4PhVpKLFinD0U4nI2nUON+D1Ghkiji4z4gmyVMSXpMCdkYb2VjSsixLmWDGfzskBuYuHVrO7Y2ATg6NcLLqy+2tgFotL60aB6Eg4NLhSwUsiyTnZ1NamoqmZmZCIJgfQHo9XrrU5koWjJXnjlzBr1ejyAIeHt7ExQURGBgIIGBgSUWHCsNvdEifuxsqlZ8vPzyyxw/fpy//65Ypct4KZGdLmfYuuoIcNuMXtCZUhBEBEDMW1o+C4iCgL1hM0Ee0QB4GgbjUafrnYe4KxyCgolv9ie6I6Fc+/UX6o4qv49AWU6TcHc1NkpzOE2+eAqA4/1CeLwahUdpyGapRMfKZ9x684HhN1zVxft0lZVSXxAEJk6cyOzZs6lbty5169Zl9uzZ2NnZ8dRTTxXqS7jtMguU7Yx5Z1K2fFJSUggPD6erK2ye1gOvJxcVsmRlZ2dz5MgR3nrrLZo0aUJKSgoTJ05k4MCBHDp0qOQLdQeSDGXkUiwRWZYxyWCQJPSSRHp6Gv7OTkgqNdoCvzUV6lOQ0brYEdC9aC6gTM1NUjddRsoxlTslgsKDg/KNVQNyXjSBlGHAlJqLKSkHc5IeU1KORXQk62+HLNqo0XjZovG2x66RB2pPO3y8WlDf5Q1EddU5fgqCgL29fZnOmG3atCl0HklJSURHR3Pt2jXOnz/P/v2WXBb5ZuKgoCCCg4OLNefficFgAMBOV3XiY/z48WzYsIFdu3bh71+xKia7bA6y230ffule1qdTudBzqmz9LCMjC7J1jUqXylQfi5hqFvQrrrWr5yZbe+jzXDu+BdsLDchNT0HnVD7rR1lOk1DJGht5szEl3Ug2vtSfOjsuIQFdXqu6sgIVRc7NLfEuein5Iq6CQO3mxYvF8qTUf+2118jJyWHs2LGkpKTQunVrtm3bVkSUy8hUxX+x1ZLVPQXqeELNmoUsWc7OzmzfXtip9bPPPqNVq1ZER0cXyihaGhkqO3Yci2Pzy2OQhYIpiixv9JKMIMvokEGSLNNyeUtBliwvSUYlmbEx3La0mkQVgz9YRGhAxfxIZOQSE7rahLnDhkvozyZjF/4gTJcrVARFfJQTWZYxp+RijM3EcDMLY1yWxUqR9yMsG83IBgkpx4Q5PRdMBZ5u1AJqN1vU7jbYhLqi9rRD42WL2tMO0UHzwHprC4KAh4cHHh4eNGtmefJIT0+3ipHo6GiioqIA8PDwoG7dutSpU4egoCDrFE5BDEaL+LDXlS8apTRkWWb8+PGsXbuWnTt3EhwcXOE+0h1zUOWq2Drqzwrv++NfHVARi0/oF7jWqN6ne3U7M/IuiJ2/m5ozyhd9UJbTZGVrbORLs+IsH2eObKfOjkucaeJKjRHPE+Zdq0ibe4bZVCgZSUREBBEREVy9ehWTyQB+IkPOtGPdZ/tLnLLYunVriVMWgiAwY8YMZsyYUeowZKrGQ8NqyVq1j8hPVlJjdlQRS9adpKWlIQhCuR4M8ons0IdaSXF4awv4ZRWISrqQpUcnioQ62qESRVQqEZWY518mCqhEFWqVCrVKROPqQVp6GllrlqOWzHi7lH/aMB9JkEAqXr6pnXVoAxzJOZmoiI9/IYr4uANZki0WivhsjIk5mG7lYErMwRifhay3lLIW7dVofB0spj7J8nOsslMjaFUItmpUTlpUTjrL0kWHylFbZl2YfwtOTk40bNjQ6luRk5PD5cuXuXjxIidOnOCff/7BxsaGevXqERYWRnBwsFWIGIwGZGS0pdS+KC/jxo1jxYoVrF+/HkdHR2v4o7OzM7a25XNorUgugjvxwVIYrr7vQ2W0vHt0tl7o0YNN+cOCy3KarGyNjZKumT4nA56yWFB6RazH3s2zXOMsK415uQqtFTdOs1Topu/v78/cuXOteVe6vNKBDREHiRp1mODgOlUyZVHsOKga8ZFvyRrb3IklvXtwJNeH8S+/TPLx0wzs0NmSMM9stiwlmRy9ngnz3qNXi7ac+2EdkllGlizbJAlMOWY0MVepLR0pdJwrg1+ni589U/oXXzgRICMjg7feeou1a9eSkJBAeHg4CxcupGXLlsW2f3v3X+iCauPiaEkKZsjOQTZL6BzLfgiRBZnSouFtG7qT/kc0ksGMqH2AnFUUykQRHwXIOhJP5oE4yPP0F3Qq1J62aDztsKnnisbXAa2fPaKj9oG1VtxrbG1tCQsLIywsDFmWiYuL48yZM5w6dYqoqChsbGyoX78+YWFhGAwGzIK5Sq5dREQEYJlDL8iSJUuKnTMvDkmWKjWWLEOq9f29CKMUnCzHUKe6I8sSQjmcZMtymqxsjQ3r+d4hpq+f2AfAya5B1C+n8ChPRE75Cq0VN9DCPh8DBgwotHnhcy/z5O73WLn8c+Z+9F2VTFkUx1mM2EmlO3qXh3xLVoPGr3M1yxE3oE3Idb78ZSsORsu5CZgRkJClXBZvX0BWtkD/xuM4ecaGnae2sOPkVm6l3wLAx7UmfZoP54VbaTjqCiRXNEFumrnUsTz//POcPHmSZcuW4efnx/Lly+nRowenT5+mRo2iDqCSkxsh12ROLdyElGbAIdMRjagjS04n1zkX2xB3ZIOEb+eGONYo/LcjCzJiKeLDJsyDtM1XyT2fgm1Dj3JeTYUHAUV85GHf0gdDbBYaH3s0PnZovO0f6CmRBxFBEPD19cXX15euXbsSHx/PqVOnOHXqFEePWlKPq1GTqk/Fxcblro51N1aLfCRZqlSmzlM3Nlvfp+fE4mpfMV+TiuLSsDE5qw4CcHXtMoIfGVHmPmU5Tbq7u1eqxoYsWe4EYoHrJkkSZz+cgZeDSI93F5f7vMoTkVOuQmvFjlMu0eHUbDaTmOyMlCsRJexnxmdDkJE5Zr5GY1UQolaLLtm7wlMWxWES4IA5veyGZeDr60vdwEBkRHyd4+g7qSPS8jbMnb+Plxa2Q1BrEFQqjEYjQ4cORXbK4cihA9bv2GOjhsdVg6lTpw7Xth9mweJfWbz1bUZu/ZMmPW7nxRE2RCLbupQ4jpycHFavXs369eut+XRmzJjBunXriIiIYObMmUX26dL7Ker9noZ0UyKLNHICDBjdVBiuGrFNtcX2kAiIpB0/SxpnSbFLBBkkowl3yYfE7BsljkfjYYvay46cM8mK+PiXoYiPPBzaKyFbVYkgCNbcBN26dePmzZts2rWJPXF76Lm6J0PqDmFE2Ah87O9foTSpMqXJgfNXv8YXyJDU1S48AFQ6GzRP6TGusEFzoBbSIBOiqvR/3fI4Td4NBUX56TO7qHMimdQXHsbVq/xWgvJE5FQaqWi0y52F/ia/8gjnA66x2uk8AP5pGjbbXiBblri8eD21m3rw3apXeXrQW3h4VC7hlpss0riEqJpynYbJxOnlqwiw9WNP5CnaDVbTvIc3Nj4BXL4RS1DNmog6Sxh8vvC4cOECO3bsKCQu8y0/cQfOcv64J0+2fIw9l//gxLULtKKA+JDzhFsJmEwmzGZzkdB7W1vbEqPNXDy8gTSMYxpSv3ZhwSuZzOiTMrmydBffbljDJ3u+L7Tdzc6JbXO+LtLnCy+8wOLFi/nkk08YWW8g2UcTkCX5PzO9/b9AhRyxIyIiaNy4MU5OTjg5OdG2bVs2b779FCjLMjNmzMDPzw9bW1u6dOnCqVOnqnzQCv8uBEGgRo0ajHlyDAteWsCIsBFsuLSBPqv78Obfb3I57fJ9GZelTknFf6ya1XsTAHvBVCUWmPLg3fgh0v32AnBz+j9kXr5S5j79+/fnxIkT6PV6zpw5U+ZN/erVq0ycOLHUNrenmW5fN31erRafthULNc73Y6hbty5bt27lxRdfZMKECfzwww8V6qdYJKmIs0VoaChRUVHs27ePl156iaVLI5nbYym/dVzGhrbfsXnCEf56Yjd+P3vgbran/lB/vlX/wztLnyE+vnJ/o/6ocRUq94xnTo1n02sRRO7z5NH2Pbl26wzxbrsxBgexYsUKFi9ezLhx4wCLKHj00Uc5dOgQP/74I2azmbi4OOLi4qxRZhnXE9j54VYEYzZyOz05Bj1t27YtdMw89/kSx+To6Ejbtm15//33uXnzJmazmeXLl7N//35iY2OL3SfpUgomAeydi+YKEtUq7LydCXttAE49gggLCyM2Ntb6Onv1Is0nFM64u27dOvbv3291BrYJdUPKMGKMzSrnlVV4EKiQ+Mh32jp06BCHDh2iW7duDBo0yCow8stML1q0iIMHD+Lj48NDDz1ERkZGtQxe4d+Hq40r45qOY/uj25nUfBL/xP7D4HWDmbhjIjczb97TsUhIxG2Mo2XLljg6OuLl5cXgwYOL+EqsWbOGXr164eHhgSAIGOLdSJd0iAIYzXc/n19e6o9/zfo+dXEMMes33rNj52MVWwVu7DlpSQA4uFYs4kCSJJo1a8bs2bMJDw/nhRdeYPTo0VZ/nrscaJHSu/mF/lq0aMGcOXNo0qQJCxcuJKhWU4JDWmI0Gnlm+LMk3Erl2NFzbHjjCE/kNmWvSyJ9fxvE8uVvYDYZKzQM0eKFUalTODhnHtHZYQwYpGfit++ydt06Vm3YSMOGDXn//fcLWbJiYmLYsGEDMTExNG3a1Dr96evry969ezk45yc+nPAzI9e9wYTvBzN+8kTWrl1bxClZQKYUwwcAy5YtQ5ZlatSogU6n49NPP+Wpp54qOe/QlXQue+vw8SjbwVStVlstpj4+Pnh6FvYByU+K9+OPP6LRWMpM6IKcEHQq9GeTy+xf4cGhQuJjwIAB9O3bl5CQEEJCQpg1axYODg7s27evSJnphg0bsnTpUrKzs1mxYkV1jV/hX4qdxo5nwp5h8yObebfdu5xKOsWjGx5ly9UtZe9cRUSnR5N+Jp1x48axb98+tm/fjslkomfPnmRl3X6Kynd6nDt37u11OktK5407mvLTnseRpKopUFcagiDgP7cjrpPzpgj/ceHc8k9IPPrPPTk+3C44W3DaxZCWAoB9BcVHSRE50dHRdzdI8nxTyjBqFSz0V3DK4o8//rBOWUwfs4ztPdfSPsOXeeZNTFnYr0LjEKmcU3JWfCKH0yx1Z3y69gZKt2TVrFkTWZYLvW4dOcuqMT9w+YckDlzzJszGzL4//mLfgQO89NJLjBgxgtOnTxc6rkDZ/lS1a9cmMjKSzMxMrl+/zoEDBzAajSWGu2tMMiZN+SyMFy5cwM/Pj+DgYJ544gkuX75tcSopKZ6gFrGp44L+fEq5jqHwYFBpnw+z2cyqVavIysqibdu2pZaZ3rt3Ly+8UHx2xtzc3EKVPtPT7945S+Hfg1al5eG6D9M9qDvv/fMeUyKnsP3qdqa0nFLt/iC55lxqvlqTkSNGWtcVV6CuOKfHYR1Ws/3EO7gk/gi5h9ixsy6pusa0aDATHztP/t5T2JxdPor7gS5+nU3rQLzPPIP9yRboT5q4+fMeVP30+HZ8iIyYi2Rev4x3q25l+oZUnKJ5Pkzplro6Ni7uxe5REmVF5NwVUuGooNIK/eVPWRw5coTffvvNOmUBlmR6HgF1+HTiNpb++jYfsZZRCzrT3bMdTzw5q8wK0CKUaUm4k6glq9iz33ItVYIJrU3Fv0NZltn+USRptv7U8kzDr56ZBiMmWMfbokULDh48yMKFC/nqq6+s+2nMJgzlPEZ+0sKUlBS2bt3KBx98UGw7Yy0n6u1LZPv3UTTpVwcvT4di27Vu3ZoffviBkJAQ4uPjmTlzJu3atePUqVO4u7uXmhTPpp4bKWsuYM4yorKvvsKbClVHhf+q73Tayjfd7d1rmY8uT5npgsyZM4d33323osNQ+I/hpHXiw04f0iWgCx8d/IiB6wbyYpMXebLek9iqq74QHUA9t3pcSr1UaF15C9QJgkDPxu+RoX+F36LeQM48gof+OKcOD+KiaLnb2NoG4+zU2PLkK0vIWDJBykiAjCxLQH5xPzlvWXCbbN1W5L2TTIr/Bswpufj/ZZmOMW+y4cKB79El10A023I16mfU7vZIgh61gw1O9ethX6M2Ks1d/DgXY/kwZaRjVIGorVj+lrIicoByFlorbpxyId1WWqG/q1evsmHDBoAiOUR27NhhDed++pF3uLH4Mts1Jzhg+o1LX1zkrZdXlXqOgiDwmzkJ25/70L/RczRr8FiZ1+VklGX5/Ns10flVLlFb1GfrSXasQ6cWBho9/2SxbQpafvLRmUzoywjl3rp1K7IsExoaysWLF5kyZQqhoaElhmh3HxDKVqDm4SQy5x/leANnWg4MwfkOH5A+ffpY3zdq1Ii2bdtSu3Ztli5dSufOnUtNimcT6goy5F5Iwa6pknDs30CFxUe+01ZqaiqrV69mxIgRREZGWreXq8x0AaZOnWotrgQWy0dABVPwKvw3EASB/rX609m/M59Hfc6nRz5l6amlDG8wnEG1B+FpV778EeVFLagLmZgrU6DO0cadJ9tYvPEvJuxl94kpBBKHJEO7tn9U6XhLpCdEr12LuN8DXXINpLopmOPUaK8FwjWQtRnIRlvSI2+RqopB1liebWVZRtbmUuOVbmgdXMp1KEm25le/vS4jE71NxZOIlycip7RCa6VlF5Xv8PkordBf/pRFWahEFdNeXM7rkpnnF3Rmi905hl49RO3ApqjF4n9KO3uEczP+H1bpY5CPLCqX+LC3MZCWC0d+3UfYYB1OgWVH4smyzM2t/2Dj6kDMwSv8c9KeAPsEwp6zOGuWZvkpiCjLZcaApaWlMXXqVGJiYnBzc2PIkCHMmjXL6oNxJ2qVSL/B9UnpkcvuLRepG5XMjY8OEf1ECI3CShYK9vb2NGrUiAsXLiCKYplJ8TT+DmQfTVDEx7+ECouPfKctKGy6e/3114HylZkuSH5FVgWFfBy1jrzR6g2erv803538joioCD47+hmtfVrTv3Z/egT2wE5z9/VhtCptoXDbyhaoy6eOVzscWv7Ae9v7odV5U/25T28TMHgwxofS0TpYwjrTrpwm7egZPDq1xM4jEENWKmnnT6KPycKUkwayjOqED7LZhEpb/orF+f4LOaac2+syM9FXsrBX//796d+/f4nbSyq0VibFhNpWFSpRxZSes3jqwMs8GvksHdN9+WL8tmLb1vNtQVLCHgRZoG1wr3L1P2BaL/Z89itHTodw5PQ5bBpFUyPEh5ZNvHH3Kv7v/uLPO9i2E0APOFJDF0e/eUOt0yylWX4KUh7xMXToUIYOHVpGq6K4OugY+GgYN7pmceHbE+jWX4RSxEdubi5nzpyhY8eO5UqK59DWj5RV5zEmZKMp4TopPDjc9YRwvumuomWmFRTKwt/Rn7fbvs3E5hPZfnU7Gy9vZPrf0/nY5mM+6PQBrX1bV7pvWZZJ1idbn3jvpkBdPrnGLB5aOxjQ8IJvoyLbd+3axYcffsjhw4eJjY1l7dq1DB482Lq9JAvhBx98wJQpU0o9tiAIVuEB4BzcAOfg286cWnsXPMM7QF4ZmoT9f2M4KqPtR4niQ5Zlci+nYU7JxZyRi/FmFo5XLKLjtb2vMUr9IkPqDoGsbIwPWlVRqXLZa8tLg4ad2Wi7jIXr32Cr+w2Sbl3H3fO2xTY+I51n10/nunkn5NSkj6EzPdq8Wq6+Za0WH6et+HmtYlvCdPQncrh04gqXVl8hW5Qx2IrYaUwEJp7D3sGBGxnO6NWOiJKRxp5xOAb70mD4EFTa299JaZafgohUTQK/0qjhbs8pf3t8TqcWWv/qq68yYMAAAgMDSUhIYObMmaSnpzNixIhyJcWza+JJ2uYrZPwVjdsT9ar1HBTungr9YpRmuqtImWkFhYrgpHViSMgQhoQMISYjhvf+eY8Xt7/I1NZTeSzksQrfZH449QNfHf+KdEM6ThonXn755bsqUJfPy79bEjm1cfNjXNvPimzPysqiSZMmPPvsswwZMqTI9jvzJGzevJlRo0YV2/ZuMOfmkr01GbN3BsGti/cHADDezCLx6xNAXvVlXzucwn2J8Uqihb4t7+97n5XnVtI/4RoaW6cqHeNdI5ec4bSqCKjdlHq+jdhquMGClROY/MSnvL73F/YnbsYsJiMAwcbWHI/pyy+SjvHXLxNQs06pfRpy9WjnBRAKIEJC/RU0f2ERR08ms2n5GSSjhJ0sok6BRDmI1JQcvF2zca8hUn9wC1zq9im1/7Ip2/JRFXhcSOdSTXsKSoSYmBiefPJJEhMT8fT0pE2bNuzbt6/cDsiCWsSpZxCpay7i2CMIjUf1+IopVBFyBXjuuefkoKAgWavVyp6ennL37t3lbdu2WbdLkiS/8847so+Pj6zT6eROnTrJJ06cqMgh5LS0NBmQ09LSKrSfwv8ORrNRnr1vttzw+4by1F1T5SxDVrn3PZ5wXG74fUO5+bLm8ocHPpTHvDBGdnZ2lnfu3CnHxsZaX9nZ2dZ9kpKS5KNHj8qbNm2SAXnlypXy0aNH5djY2EJ99/upqdzw+4ay2WwqcxyAvHbt2lLbDBo0SO7WrVu5z628XFuzRo5+4y855fLxUttlHo6Tr7++SzZlGordfjzhuPzS9pfkJQPqy8cbhclxs+fI5tzcKh9vZTgdWk8+HVpPTlj0ebUeJzM1UQ6eGiw7NnGU1S5qGZAbjO8qD1/9nrzriuW37+OIL2X7ht1llYObbGtrK/fq1Us+f/58sf3t/2mWLL/jJJ+b3a7U4yYnZcqnQuvLp0PrybuXrpFP7TooJ8YmykaT+a7Op+uK3+QXl6+9qz7Kw1/z9srbvzxU5f2ac01yzJt/y+k7o6u8b4Wyqcj9u0Li416giA+F8rLx0ka51fJWcv81/eWTt06Wa5/V51fLDb9vKG+7ahHN5IeP3PFasmSJdZ8lS5YU2+add94pPJ7TX8oNv28obzz1aZnjKEt8xMXFyWq1Wv7xxx/LdV7lJSvhpnxt2nb50g/Lymyb+ONpOe7TI2W2i40+I1//5CP5TMNG8uUhj8q5V69WxVDvikv9+sunQ+vJ51q3rvZjvTSxn1yjt6cc8HKADMi/rl5t3SZJkty6dWu5Vp26ss8z8+V58z+Wx4wZIwcGBsqZmZmF2mXlGmX5HSdZfsdJvnx8d5nH3fX8BKvIyn+tb99b3vjGXNlUSRHS7aff5DHL11Rq34rw2y8n5fPTdsmpKdllN64gt344JccvOlrl/SqUTUXu3xV3UVdQeEDoX6s/P/f/GVu1LU9uepLpf08nLiuu1H0ScyypwL3tLE7Q8h2JmfJfBZ0cR44cWWybO6Mt+oaOxlUt8Me14p0PK8LSpUtxdHTkkUceueu+ChK/eieSykCNwaWb501JOeScTMKuSdkRRj4B9fCf+H8ErfwJc3o6lx9+hMSICMyZmVU17ApT67eNqGvUQDaVXqG1Kvjik9/Yv/AA6/9yBUBVIPfHhQsX2L9/P+tWr+bpxrZ8ER9KoldTUlNS+OmnnwDQ52STNCMQu9kWn4aDLn0IbtShzON2/HohIUcOs6r/IHokJ9Pk0kVePxxJyo9fcD4sjG1vzGL9sxP5vdcQVgx5nnUT32XN/83i0q79xJ27jDG3aEYPUZa5W5cPWZaRJQnJbMZsMmLMzSUzO5vkjAzi0tKITk7BNdwZM/DP+rN3d7BisK3nhuF6BpKh+r97hcrzgHmJKShUjJrONVnRbwVrLqzh86jPOZl4kvWD15fYPjnHkoK5OhKYiaJIC7eabE+4QnLWDdzsK1+s8LvvvmPYsGFFCnjdDUknD6G56g/dk9E5lpwQTJZkUtZfQrTXYN/Gt8R2d2IbFkbwmjXc+nQhiV9EkPjVYhw6d8auVUt0deqidndDGxSEcDd5RiqAoFEj5eZy67NFSAYDop0dSBKyZLZEw5jz3suALFm2yVi2yZKlwFpe5lhZkjBcvow5LRXb8PA8PyOBXIOJPTe1OF+Pwl8u6i2Rn0fDzt6OeRPH0HnrJl7eFUiWpOadT7/jr+u5PKzew2OCJbniyRYzadHnpXKf468bNzLz0/l88cUXtG/fnojPP2fMV4v5LTCQgHXLSXR0RzKZCb52mvRLDuiMuRg2LccApADnA8MYtO3XgletwvlYtxw7QeRPS3G7eh5BlsvdQ5hLe+qZ2pB86TputasuvYLKxRI9KWUaEd1KSPmucN9RxIfCvx61qGZo6FDsNHZM3T2VTEMmDtrisyim5FpSMHvYVE/57SH1RrA9YQYf7Z3I7IdKT0BVErt37+bcuXP8/PPPVTYuyWwmfeM1ZBczNbuVbE2RZZm036+Qez4Fj2fDELUV+/FWOdjjM20a7qNGkbZhAxnb/yB+7jwwWmqiqL28cBk6FNennkRdRiK3u0Xt6obx6jUSP/+8Svs1XLhY6HMzwCyIXGjVA86cLLStXr16BAUFMXXqVL766it69ujF6MNzeTMrBRtTBvtzA7A3hhJY043Woz+jYQWdZOfPn8+oUaN4/vnnAfh00SK2//knfwwezFvT36KevW0hh+ykuCRO/nOMzPMXqLVkASHRp0iIS8TLx/L/ICIjlcP0EZeUxOnz59ixfRsupw5h6+aFqc8QVDob1KKAShRRiyIqUYVaJaISRTR569SiiFqlQjYK8JfEtZ/24vbm4xU679IQHSzJ7ozxWajdqk68K1QtivhQ+M9Q16UuAOdTztPMu1mxbdJy0xAQykyLXVGup15g16XvOZFwBIAaDpUL1wVLWGTz5s1p0qRJVQ2P2D9/R5Pmhe1wXYkp182ZBlLXXyLnRCIuA2tjE1p5caDx9sZj9Gg8Ro9Gys3FeOMmplu3SP/9d5K+/ZaUZcvw/3wRdi1aVPoYZRH4/RJyoo4RM348stlM4PdLEEQRQRRBrQaVyvJ3IKpAFEAQEVQiiJaXIAigUlmiZkSxQLp2GWQZk8FI4zmRyILAF73q8HCPhvDDp4Wvg0bD6tWrGTVqFG5ubqhUKnr06GHN5nnT1oaT2g60HtO9wudnMBg4fPgwb7zxRqH1PXv2ZO/evdg5FM114e7jTueHuwHdOFC3Jo7TJpLUpSP8thmvOjURJQmphP+NTKORTz7/DOnMcRxSLdOXGgcnPJ94jokDBqJSV/x2cv7yH7hd9eLab/sJ6l/50PmCaHzsULno0J9PwbZ+xVL+K9w7FPGh8J+hlksttKKW00mnSxQftmpbZGRS9am42LhU+lhmycxPx+bw8/m12Ku1xOXmkGS0zDH3963DuLafFNknMzOTixdvPzVfuXKFqKgo3NzcrJkb09PTWbVqFR9//HGlx3YnuenJmHarMdeKxj9sWJHtptRcsg/FkbH7BgjgNqw+do2qzjIk6nToagWjqxWMfetWeL4ygZgJE7gx+f8IXre22iwgok6H2sMdKT0dlYszdo2K5l65G9Yv20yuWkuf7Ct0atuzxHbNmzcnKiqKtLQ0DAYDnp6etG7dmhYtWnBTKK2AfekkJiZiNpuLLWmRX5umNFo90ou/33fGPSeNpP592BveGVWfXph1xU+LRfy0Att/dqBv2QHX+o34c/NmVny+CL77iRHFHHvGjBmsXLmS69evo9Vqad68ObNmzaJ169sio9bIjpyetRHX3Squivup2ffuBYggCNg29CA7KgGX/rURVNUbcq1QORSHU4X/DBpRQz23ehxPPF5im0G1BwHwy/lfKn2cA9EbeXR1W+Yd/xlHtQ4blQ5bUcXYup34v9oNeLNz8QmdDh06RHh4uDUJ3+TJkwkPD+ftt9+2tlm5ciWyLPPkkyXn36gIJlMG139eh8rogHvz1ugvppB97BYZu2JIWXuB+AVHiJt3gIzIGOybe+MzpWWVCo/iULu5UePj+cgmEwnVnIDwyqOWdOaOfftWed8LjyRZlh+OQWtfdkZNZ2dnPD09uXDhAocOHWLQoEHlqiJbFhUtaVGQ9kf+4fon3wMQejQSc1aWxTIEXI1P4PsdO/l21S8sWvIduX9uwtC0FW+++jrP9etLXQ93wsLCiI2Ntb5OnDhh7TskJIRFixZx4sQJ/v77b2rWrEnPnj25deuWtY3aVkfYO4PJVKeh3mUg7kjVOKDahXshZRrJOZ1UJf0pVD2K5UPhP0Vr39b8cv4XTJKp2Hobnfw7IQoif1z7gzGNx1So77j0i8z5+xV23LqGn1bDJ21eoUfo8+Xev0uXLmXeaMaMGcOYMRUbV0lIkoGoY8/jnGgx8WesiiUDSyIzQatC5apDG+CIQ6ca2Ia5I+ru3c+BxtsLtxEjSPzyS3xmzEC0rfqEUCm//IKck4Ndu3b4FhB4Vda/aHFsTLxxk4T0VOv6Oy1aq1atwtPTk8DAQE6cOMErr7zC4MGD6dmzJ2/s317p6BIPDw9UKlURK0dZJS0KIggCD/VuxeKrc3DcsZVMW1t8U1N454O5OBzZiyhLmEUVuVobjG5ePDfqxUL7q9XqEgv83Zlccv78+Xz77bccP36c7t1vTzOpNGqCxnUg+dOTpF+Nw6fZ3Wcn1dZwQFfHhfRtV7Ft4K5YPx5AFPGh8J+iW2A3vj7xNQfjDtLWr2hZe1EUCXAM4GLqxWL2LpmVx+Yy//iPCMCLId15vuVctNVUbbcqMJtzOHFyPOnpxwke/QrO6mbIRglBFBDtNQg6VbWmHy8PTn16c2vBAlJX/YrbM8OrvP+svXs5lJ3NijOnOebnV2xK+/JMDRTHib1RpOfVFzoffZWuXbtat+UXyhwxYgTff/89sbGxTJ48mfj4eHx9fXnmmWd46623APIsH5U7v/zxbt++nYcffti6fvv27QwaNKjc/QiCwAsvDWaHnYHBvy8BwKDR4dD/MTp17ETtGn7YqIufirlw4QJ+fn7odDpat27N7NmzqVWraCVeg8HA4sWLcXZ2LtaXSeNg+V+SjFWXX9W5d00SFkWRfSQe+5ZVH92mcHco4kPhP0WYexh1XOrw87mfixUfYLF+LDu9jOO3jtPYs3GZfe678jNzopbT2tWLmV2/xcux8inY7wWpqYc4c3Y6en0MTRovxt293f0eUrFoAwNxeXQItz7/HKcB/VG7ulZp/7lnz5EtyzTr1o3RzZsXm6Y+f2qgVq1a5OTk8Mknn9CzZ08uXryIp2fJOU7OXr09dVDTO7BUi9aECROYMGFCsdsE4XaxvsowefJkhg8fTosWLWjbti2LFy8mOjqaF198seyd76BBt3Yc+f0HMus25amxY6nr51dq+9atW/PDDz8QEhJCfHw8M2fOpF27dpw6dcpah+W3337jiSeeIDs7G19fX7Zv346HR9FpPTG/Do2x6nJzaP0d0YW4kqWIjwcSRXwo/KcQBIEn6z3JrP2ziMmIwd+xaNTJk/WeZNnpZaw8u7Jc4mP+kYWIgsBnfTai09hXx7DvGpMpk4SELcTGrSY19QCOjg1p2XIdDvZ17/fQSsXzlVdI/30ztxYuxPeOpG0VRTIYiHv3PbIPH0YQBAxXr9KzbRuCZ80qcZ/yTg3cyYWYFMCed+tCzfpFn/TLiyAISFLlxcfjjz9OUlIS7733HrGxsTRs2JDff/+93PVQCpKelQ1Ak459yhQegDViB6BRo0a0bduW2rVrs3TpUqv1p2vXrkRFRZGYmMjXX3/N0KFD2b9/P15ehavZihpLSHdlLR/JV+JJvBiHg48zcQeuIEggygJ2KSK2KQIZ15JwDFIiXx4kFPGh8J+jf63+LDq6iG9OfMOMdjOKbA9wDMBObcf+uP3l6s/X1pU4fdYDJzzM5mwSE3cQn7CJpKSdSJIBV9e2NGz4GV6evQuEhj64qD088Jw8ifj3Z2LXsiXO/fpVqh/92bNcGzESKS0NNGowmgDwq0DUUFlTAwUJchAhGVyd7m7qTRTAfBeWD4CxY8cyduzYu+oD4FZSKgDubs6lNywBe3t7GjVqxIULFwqtq1OnDnXq1KFNmzbUrVuXb7/9lqlTpxbaN98nQzaXz/Khz8jm9Pd7UdtrES5k4So7YAdIpOOFGr1oRELChJEk0cipvXvpFTSgUuelUD0o4kPhP4edxo5RjUbxyeFPeCbsGWo5F30ybeDegEPxh9Cb9NioS09E5GfvzYGk6OoaboWRpFwuXfqYmBsrkKQcHB0bUSt4It7e/bGxKfuJ9UHD9amnyDl2jJuvvY6ck4PzkCEV8kdJ/Pobbs2fD4Dn/0225BYxGJCNRlT2ZQvG8k4N5CNJEofjsgFb6tWvuIWhIALCXaczrypSsyyJ4NzsK5eYKzc3lzNnztCxY8cS28iybM36eieSbEZOl0g+dRNJkpEkCVmSkGWZfw7v54sfvuL42VMkJCbw6StzeNimvaVPLN/xIddrfLr2aw6fiUKSJMLCwvjll19IiYlh/x9/0CqlA65VPLWnUHke/EcjBYVK8Hjo4/g5+PHW329hNBuLbO8bbAm93HhpY5l9OWqdyMn7MbzfZGdf49DhoVyPWUZQ4Gjatd1Bq5brCAoa868UHmCZevCbPRuXIUOIffMtYl58iZxTp8rcT9LrufrUMG59/DGikxPBa9fgMXo0AKJWWy7hAbenBvbu3Uvv3r0ZOnQoCQkJJbbfv/MwawzuNNEnENKwdvlOsgQEofIOp1WNIa8Ojq2ufFltX331VSIjI7ly5Qr79+/n0UcfJT09nREjRpCVlcW0adPYt28f165d48iRIzz//PPExMTw2GOPFX98SY9LgjPZyy6h//Eyhp+uYvw5GtMv18ncFk19sz8zO4y3jDHa8r+Y3cUR/zkdyR3tx6gPXqFFp9bs3LmTY8eO8dZbb2FjY0OLFi2wtbUlMjKyCq6SQlWhWD4U/pPYqG2Y03EOz255lhn/zGBm+5mFnqYH1B7Ae/ve4/crv/NYaPE/hvk4al0wI5BjTMde51LNIy8eWZZJSNjEmbPT0WrdaNFiFU6ODe/LWKoDQa3G9713cejUkfh5H3B1yKPo6tfHuX9/XIc/jajVFmqfc/w40c+NQsrMxK5dOwK++hKxkjVjyjs1kI+/vyeCHMcJGw+mv/sFWm93RJXlOU4UBGxtdHRu2RhkGbNsEa2B3u74ebgUPe+7SDJW1VidZss5XRcTE8OTTz5JYmIinp6etGnThn379hEUFIRer+fs2bMsXbqUxMRE3N3dadmyJbt37yYsLKzY/jbHL8erVhgt+g22ZKIVLJmIRVGgq/go3YTHLBlp172FuocPjg+H4e9rSVA3ffp0+vbtywcffGDtr2DUTceOHdm6dSsdO3a0OsMq3F8U8aHwn6WJZxPea/8eU3dPxdvOm/Hh460CxEZtg7edN6eTTpfZj5ONxVSbmhN/X8RHRsYpLl3+mKSkSLy8+lG/3izUasd7Po57gWOPHjh06ULGX3+RsWUrCZ98Qurq1fi88w72rVsBcOuzRSR+8QUIAt5vvYnbsKJZW++G0qYGAAJCajI39CQrL99AIydgiEtGyrth62QDRmDjxcNF9stxCebZx/ojSTKybJlOUJtzQX4wclDIeYXxxHIOZ+XKlSVus7GxYc2aNRU6fi65mGyM1G5Zv8y2Ln5uOOcJD0mS2LRpE6+99hq9evXi6NGjBAcHM3XqVGtYdfPmzdmzZw87d+4sNupJ4d6jiA+F/zT9a/UnMTuRjw9/TGJOIlNbT8U2Lz9Ha9/WbLi0gevp1wlwKrmqppPO8iOXpo+nBqH3ZNxmcy5JSTu5HrOU1NT92NoE0rjRV3h4dL/v+TmqG0GtxqlnT5x69sT9/Hni3plB9IgRBC77gYR589CfPIXK3Z2g5cvQBZce9lxaSnt3d3dmzZrFwIED8fX1JSkpiS+++KLUqYF8WtVx4uKFvfiYYdScGdb1BpOZyCNnMUsSoiAgiCJXYmK5eWw3tqlXWPn1Z4X66QbcwA3ow32ngpaPqkYQRczGolOkZZGQkEBmZiZz585l5syZzJs3jy1btvDII4+wY8cOOnfujEajoVOnTmzatIn27duXmBhN4d6hiA+F/zwjG47E1caV9/e9z5GEI7wc/jIPBT7EE6FPsOHSBlacXcHrrV4vsp/BbEAjanDWWZwP0/SJ1TpOozGFxKRIEhP/IikpErM5EyenpjRs+BmeHj0Ri8nY+l/HJiSEoOXLONeyFdEjRoIk4dCtGzU++xRRVbZvwqFDh0pMAPbll19WeGoAYOUXX3A2IQFnyUyXXr0KbdOqVTzU6o59WzYgqWsrdh0+aRGOgoAoWKYV9kduo4aUzOvzIqzWEMv0h0zb1q0Z0r1qiq2Vh/xqtuL9EreiiNlsqvBu+b5YgwYNYtKkSQA0bdqUvXv38uWXX9K5c2cAmjVrxv79+/n999959tln//Mi/kHnf+/XTOF/kkF1BtHQoyEfHfqIKZFT8LT1pHugJY/DH9f+YGjoUEySicScRM4mn2XPzT0cjDtIY4/G9AyweNWn51Z9nQhJMpKQsJkbN1aQmnYYkHB0bERgwHN4e/fD3r5OlR/z34agUqGyt8eUnY3vrJm4VMBsXlZK+4pODQCczXNGHdC9O7W7dSvXPu4ujjzcvWjSuwy9kaijRxCQEfMq7gqCgDbjJqdOn7mn4iPf8iEUqGorSxI5N84S889mrkYdQKOCpAwZLxc10dGJxGdpad/chzavfXPXhxdEFZKp4knGPDw8UKvVNGjQoND6+vXr8/fff1s/q1Qq+vTpw7Jlyzhx4gSNG5ed40eh+lDEh8L/DLVdahPRI4KTiSfZdHkTkTEW7/e47DgGrhtobWertqWJZxMmNZvEH9F/8MGRzwG4kbibLP+uVSIITKZMbt78mejrS8jNjcXNtQP1683C3b0LOp1X2R38j+HQtQvZR6MqJDyqC6esLARJwr9ly7vu66lebXmqV1FRMnHmpzibDNbPMQkpZOpzUYmixQlTENFgQCcYEEQNokoNogpBVCOo1JZlAecNrUpEpxZLfdrPF2mxO3/m0MGd3Ew0YJAEJNkiRgRkZCz7pyTqscmrBRR7PY74/b8jSyZkwKNpdzS2Fc+JI6pETIaSfW1KQqvV0rJlS86dO1do/fnz54skW6tduzb169dn27Zt1KtXD+0djswK9w5FfCj8z9HQoyENPRryeqvXScxO5HrmdcySGbWoxt3GHT8HP1SixaQ/ImwEUQlR/HjkLVz1Uezb3xtf30epXWsSOl35inflI0m5pKUdJTHxL27G/oLZrMfHewABgaNwdLj7Ylr/ZbRBNUn7bVOFKrZWB7Isk2VjQyMbW2yqMWeErNJgSotjyvsfoTIbsMFQpM14luBOaol9mGUBEyrMqLARLDf1G8+foIZ/YAkHtVgddv/2J75OZpo2DcLO2QV7Dz88m3TBpXYjEk/8jZ1nDey9a4Iosmb8QC4nSFye/4W1m9bNfqfD619W+Jyd/QJIOn2Mv9evpsOgwiKzNN+dwMBApkyZwuOPP06nTp3o2rUrW7ZsYePGjezcubPIcXr27Mlnn33GgQMH6NChQ4XHqVA1CPLd1nOuYtLT03F2diYtLQ0nJ6f7PRwFBSuSZODGzZ+5fHkBkpRDDb8n8fV9BAeHBsXeEE2mLNLTo0hNO0Ja6iFS0w4hSXo0Gjf8fB/FP2AENjrF8a08ZO0/QPSIEQT9tAK78PD7Mobc7GyWv/su121t6VkzmHYjR1Tbsdb/fZyjx08gSRKS2YTJkEt406ao1SqLb4YkMfDvAUS7tiO7/qMW4SCZQTIhyiZkSUKQTYiyGdlsouGJOda+40fswTu4aJj2ztVLOPXrSlq1q0+LcfMLTb+URE5CNGmXohBEFYJKzabPPiIg0Ise7/9Y4XPOyczki1FPAPDUB4vwDap5e2w7dxby3cknv3gfwHfffcecOXOIiYkhNDSUd999t8QCexs3buT06dNMnDgRnU5X4bEqFE9F7t+K+FBQqCAmUwbR0d9xPeYHTKZUNBp3HB3qodG4IogajIYk9LlxZGdfQpbNqNXOODs3w9W1DW6u7XBwqPevSH3+ICGbzVzq3QdtzZoELP7qvlg/VsyaxcXcXLq4utJ+/HgWf/01ERERXL16FYCwsDDefvvtQjVPzpw5w+uvv05kZGShrJuBgSVYHyqAaYYb12sNI/iZz8pse3ZOR+rlHgfgwtAduHv5c+SvX9ElnqBGyiFqGc9b26a/fBonjxqVGtOK0X3R6lQ06z0AvzZ9sfEoWlupNK6dPc2v77yGR6NmjHjzvUqNoSRkWSYlPo74mOvEXLnMgT27adWjFw/1H1j2zgrloiL3b2XaRUGhgqjVjtSq9Qo1a44lJWUfaWmHycw6j8GYhCQZ0GrccHFpRYD/Mzi7tMDerrYiNu4SQaXCe+pUYsaOJenrb/AYM/qeHv/s1q2cNxrxMpnolBdR4e/vz9y5c6lTx+IDtHTpUgYNGsTRo0cJCwvj0qVLdOjQgVGjRvHuu+/i7OzMmTNnsLGpXPryO1FjRmXKLFfbbP8OcOk4kixQ9xeLBaFHMe32eT9JG/fKZ8p1cNBy4aaJa8s20PzIXrq8/X2F9g+q1wCNuxfpcTcrPYaCmEwmfpj1Dmk3ojFnpiMUqB1jA1zYvJ4e/QYokS/3AUV8KChUElHU4O7eEXf3kmtZKFQdjt264jF2LLfmz8ecnIznpImI98hk/tuOHWBjQ/PQ23leBgwoXKhs1qxZREREsG/fPsLCwsrMunm35KLDqHYoV9vwYbM4FhlO+pmdmGXocutH4tQ1MI/YxNXfP0GXeJLmU/+gTTmmWkqj3+zl6BOusuLN/8OQW9RPpTz41Avj+p4dbPtpGecj/yA3M4MeL75Ckw6dK9zXvp07SDl9DICANp1w9w/E3a8GXn41yE1LYe2cdzj3z27qtetUqbEqVB7lcUxBQeFfg8f4l/GeNpXk5cu5PGAgaRs3Ihsqd5MrD1f37GHpG1PJtLHBKSeH8BKSj5nNZlauXElWVhZt27a1Zt0MCQmhV69eeHl50bp1a9atW1dlYzMJaoRy5sUQRJEmXYfScewXdBn3BcxIw+fN0/ywfCUdXvyM5bcaW308ZFlmxowZ+Pn5YWtrS5cuXThVjlo7ACpbB+yDGmJvI5KWmsmFTd9xYfMyzm9Zya2zR8s+J6OBgLwIlRPrfiY3JQmMBs4e2Feu4xfpz2S5Pq2eGcPjk16jx2NPEN6+IzWCa1GraXNqNW/F7hVLMVUiuZnC3aFYPhQUFP41CIKA2zPPYN+hA/Hz5nFzymvEz52HyyOP4Pr0MDTeFYtAKg3JbOb77dvBRkdHL2+6jH4e1R31Y06cOEHbtm3R6/U4ODiwdu1aGjRoQFxcXJlZN+96fKhAqvxN8+DBgyxevLhIvosPPviA+fPn8/333xMSEsLMmTN56KGHOHfuHI6O5Uvrb2un5XKcmegfbudREZF4ds48XGo1AixC42rUES4fOUDsxfOkJcRh1Out7QVRpEZoA65fuoCxEiG4AIE1a3IYsCkhpLbTU8+ydMo4orZspMWARyp1DIXKoYgPBQWFfx26WrUI/Oorci9eJGXlz6SsXEnqr7/i/+lC7Kog/8bpTZs4tHcv5ImN7mNfKrZdaGgoUVFRpKamsnr1akaMGEFkZCQuLi5A2Vk37wZJEEGqeEZQsISuDhs2jK+//pqZM2da18uyzIIFC5g+fTqPPGK5GS9duhRvb29WrFjBCy+8UK7+B877idzEa2DKRTDp2bnoPU7Hivz49lScatQiNT4OQ062tb2oVuPg6kZgWBMCGjamXvvO2Du7APDxyMcrlXYdwMbWUkrBUEKtHnf/AOq0aEPk8u+o1bw1bn6Vc7RVqDiK+FBQUPjXoqtTB583p+Mxbiw3XpnItWefw/Pll3F7dmSF/UGSTp/mx6VLEYxGkpycQKOhPtD+Dt+Ogmi1WqvDaYsWLTh48CALFy7ks88+K1fWzbtBRoVQSfExbtw4+vXrR48ePQqJjytXrhAXF0fPnj2t63Q6HZ07d2bv3r3lFh8qGzvs/C0F4m7s38LpWMuUjt4IhuvXsHdxw79+GIENm1C3dXucPDxL7EtQqSotPnQ2+eJDX2Kbto89xYUDe/ltwVye+aDsyCGFqkERHwoKCv961K6uBH77DbcWLuTWokWk/vILrk8/jetTTyKWI7ok9eJFvv9+KVk2OtwFgTpZWTw6fTo2eRaM8pJfEbciWTcriyRUbtpl5cqVHDlyhIMHDxbZFhcXB4D3HdNX3t7eXLt2rVLj9KjXCpABga69WtPsubcqtL8gqjCbKik+8iwfplL8gjwDa9J5+Ch2LV9CwtXLeNWsOqdghZJRxIeCgsJ/AkGjwevVV3F+ZAiJX3xBwvz5SFlZeI5/uVA7WZZJOn2aKwcPEn31GrHZWSTb2KDWqBk1YAA1yjltM23aNPr06UNAQAAZGRmsXLmSnTt3smXLFoAKZd2sDJJQccvH9evXeeWVV9i2bVupIb93hp7eTWZZnbMbw6ZMZFvEJ+za9g81Wh7Fu1H5E8WJKhVmU+UsPFobi/XLWIZTcnjvAZz4axt/LfmSx2fMU0Jv7wGK+FBQUPhPoasVTI2PPkTKyiLn6BEMyclc++cfrp0+zY3EROKB7LwnYge9Hh+tjoY+PjTs1QuP2rXLfZz4+HiGDx9ObGwszs7ONG7cmC1btvDQQw8B8PDDD/Pll18yZ84cJkyYQGhoKKtXr66ylN6yoKqwz8fhw4dJSEigefPm1nVms5ldu3axaNEiq6UmLi4OX19fa5uEhIQi1pCK4NPiIR7/qBHfjB3B1k8+5pnvlpd7X1GtJvNGNJ+OG4UsWSr/IkvIUt4yL+OrLEtQcF3eS8DiPFwaKrWabiNf4NdZbyqht/cIRXwoKCj8J1G1asm6AwdI+PRTAESzGQ9RJMTFhaA6danVtg3OfpVPqPXtt9+W2ea5557jueeeq/QxSsPTGINdRsm+DMXRvXt3Tpw4UWjds88+S7169Xj99depVasWPj4+bN++nfC8NPYGg4HIyEjmzZt3V+PVufoQ5pLE4WQNKXEpuPqUrzZOvY5dubBvD4IoIAh5lX8LvESVClFUkZqVxaptf3H8wkUMRhO+nh689NTjhNSuRbv+g5kxYwYrV67k+vXraLVamjdvzqxZs2jd2lI5OKhxU4LDW7D3l+WEtG6PqFLd1fkqlI4iPhQUFP6TOPbtS0Ken8KIvn3xDw9Hc0eo7INMTkoct/atBLMp70k+7+ldkkA2EwTYGxIr1KejoyMNGxau62Jvb4+7u7t1/cSJE5k9ezZ169albt26zJ49Gzs7O5566qm7Pqd6TqlEpfjyw+vv88rS+eXa56HHh/HQ48NKbZOSkkJ4eDhdu3blk8Xf4OXlxaVLl6hZsya186xZISEhLFq0iFq1apGTk8Mnn3xCz549uXjxIp6eFofX9o8PZ/kbr3Aq8k8adetZ2iEV7hJFfCgoKPwncfT25qGHHmL79u1k2Nj8q4QHQNymuQRfXFJqmyuBjxFcxcd97bXXyMnJYezYsaSkpNC6dWu2bdtW7hwfpeFkYybQyYkraedZPG4SCDps7B3pO340Hv5ele533rx5BAQEsGTJ7etVs2bNQm3uFE/z58/n22+/5fjx43Tv3h0A7+DahLRuzz+rf6J+x66o/2V/M/8mlMJyCgoK/1lkWWbdunUcO3aMrl270qFDB1T/EnP65aVjqXFlFcKUC5aqsYKIIKpAEAp8/nclqc58pw4Jbv3Zdd2NzJQERJWanPRoRJUNzfs9Taen+laq3wYNGtCrVy9iYmKIjIykRo0ajB07ltGji68BZDAY+PTTT5k5cyYXL17Ew8PDui0p5jpLXx1HlxGjadan5DBrhaIoheUUFBQUsERtDBo0CGdnZ3bu3Mnx48fp3LlzkayeDySyjEnQYG/vcr9HUmXIqNBpYORHb1vXHdmyi8hl33Bw/RcYcnLoMWpIhfu9fPkyERERTJ48mWnTpnHgwAEmTJiATqfjmWeesbb77bffeOKJJ8jOzsbX15ft27cXEh5gSTwW1qU7/6z6kXrtO2Hn5Fz5E1YokX+XbFZQUFCoIKIo0q1bN0aPHo3JZGLNmjXklpDx8kFCkM1I/7GfaAl1kdwkzXp34pWlS1BpXDi7ZzuVMcZLkkSzZs2YPXs24eHhvPDCC4wePZqIiIhC7bp27UpUVBR79+6ld+/eDB06lISEhCL9dXxyBLIss3Pp15Uaj0LZ/Lf+shUUFBRKwM/Pj3bt2qFSqdDdo2q4d4UsIf/HfqJlQY0gFw0PFtUqWj8yitysGPat3VHhfn19fYvNJhsdHV1onb29PXXq1KFNmzZ8++23qNXqYqOW7Jxd6P7ci5z5eyd7V62o8HgUykaZdlFQUPifQafTYTabyc3NfeAFiCDL/NeeuSXUYC4+W2mbh7uw95dPOfL7GlQaEQdXFxp0aFquftu3b1+pbLL5GWmLo37HrmQkJ7F7xfc4uLrS5KHK+aMoFI8iPhQUFP5n8MvL63Hp0qUiT8oPGv814QEWy0dJidEEQcAruBUJl/ewe/lHAKTGv0K7IQ+V2e+kSZNo164ds2fPZujQoRw4cIDFixezePFiALKyspg1axYDBw7E19eXpKQkvvjiC2JiYnjsscdK7LflwCFkpSTzx7cROHp4Uiv87osWKliokE1vzpw5tGzZEkdHR7y8vBg8eHARtRkfH8/IkSPx8/PDzs6O3r17c+HChSodtIKCgkJl8PLyIjAwkL179z74c/myhICQ99aMOauob8K/DYv4KLlOy7BZr/PEuxG0f+JVAPavWY7ZLJXZb8uWLVm7di0//fQTDRs25P3332fBggUMG2bJD6JSqTh79ixDhgwhJCSE/v37c+vWLXbv3k1YWFiJ/QqCQJdnnqdWs5Zs/uxj0m/9+7+DB4UKhdr27t2bJ554gpYtW2IymZg+fTonTpzg9OnT2NvbI8sy7dq1Q6PR8PHHH+Pk5MT8+fPZsmWLtU1ZKKG2CgoK1cmlS5dYtmwZ/fr1o2U567hUJbIsk5mZgFarQ6dzKbLdbDZy4cIfpK+cQyuOkeTjgXucJZmYWYT0No/j3P1zRNW/KwdF7LyH8c35i1i7nvi+tqrUtrIss2Ty26TcPIqtUy26jHieBh3uX4SSPjOTZW+8gp2zM4/PmKfk/yiBity/7yrPx61bt/Dy8iIyMpJOnTpx/vx5QkNDOXnypFVNms1mvLy8mDdvHs8//3yVDl5BQUGhMmzcuJFjx47x/PPP4+Pjc0+OKcsyUcc2sG3rPnJyLP4m3j5ZODs7sn37cf78Yx+xsfE4OjrQpElTJndyYICwy7p/mp8/DrExqGQwqgSymw1EHdwT+wZP35Px3y3md9yRBA3pfVfi3qpLufbZuXwjRzf/jGRKRRAdGPvt99jYlV2luDqIu3SBlW9PoVH33nR/7sX7MoYHnYrcv+/KlTotLQ0ANzc3AKvjTsFqiSqVCq1Wy99//11sH7m5uaSnpxd6KSgoKFQnvXr1wsPDgx9//JH4+Pi77k+WJSTJgNmcjcmUgdGYQq4hkRx9LNnZMVy+/AcLP32J9euOYmNjokMHH8LDnTGbVSxZ8gc/r/ydDh06MW7cOEaP6cOBA/s459gGZqTB2ynwTirOY06heieNm/XqoTHLOB9cj/0v4zDMciHjr8lVcFWqFxM64oNeLLfwAOjy9ADGfv01QU37IEuZLB47lpzM7OobZCn41K5Ll2dGE7X1N87u3VX2DgqlUmnLhyzLDBo0iJSUFHbv3g2A0Wikbt26tGrViq+++gp7e3vmz5/P1KlT6dmzJ1u3bi3Sz4wZM3j33XeLrFcsHwoKCtVJRkYGy5cvJzExHq02DUHUgSyQ/4tYcFlkHRRqCwKynOefUWh5+/lOrdHTqWNt2rZ9Bo3mdqRNv379sLODZ5/rSe1aTQkN7cyQIUOws7Nj2bJlxY49N/0y+us7MP01HfekHACyHewxtBqOc7v3EdTau7w6VY/+nRokBT1PjeeK/t6Xhx+nzyLu4j8APD7jM/zrV3Vi+bKRZZlNn37I5SMHeXrOJ7j5+d/zMTzI3JNpl3HjxrFp0yb+/vtv/P1vfwGHDx9m1KhRHDt2DJVKRY8ePRDzUgD//vvvRfrJzc0tFOqUnp5OQECAIj4UFBSqHYPBwIaN75KSct7i2inI2NkGoNY4Iwh5VVQFETFvKQgioihiMmWSnX0Jnc4Le/tgBEFl3ZafBl0UVYhC3lJUERraAg+PoqGfc+fO5csvv2Tbtm2EhIRw7NgxevbsyYIFC3jyySfLPAdZMpO2922EE6twjo+3CKOANgh2bnktBBCEvLfC7XXWzwXWCdzxWUCSZdLT1eTYBN+xb8FR3N5HvuNz/tLv/HvEBEzAf9T7ZZ5TcUiSxCdPDgSg/6S5hLZpWMYe1YMhJ5vl0yajUql4aubHaGzuzzTQg0i1p1cfP348GzZsYNeuXYWEB0Dz5s2JiooiLS0Ng8GAp6cnrVu3pkWLFsX2pdPpHvh4ewUFhf8mWq2WR4fMIjPrAlevfsGtW1uRpKPotN5odV6o1Q6oVPaoVfaYJT0mUzrZWZfJNcTj5Axubh0Jb/rRXY3h9ddfJy0tjXr16qFSqTCbzcyaNatcwgNAEFW4dJgFHWaR+esQHE7+Adf33dWYCiICLoC9VOB3Wrj9zHpbg+TLjjufZ2VrQ5Vn5a0VYoE6NlHbdt838aG1tWPg5Kn8OH0y27/5nD7jJiMIQtk7KhSiQuJDlmXGjx/P2rVr2blzJ8HBJf8hOTtb8uFfuHCBQ4cO8f77lVO7CgoKCtWNg31dGoZ9gsmUQXLKXjLST2AwpmA2ZWIyZ6HPjUMl6tBoXPH1fQQHxzCuR3+LSmV718f++eefWb58OStWrCAsLIyoqCgmTpyIn58fI0aMqNh5PLqaJM9JuO/4jpTuL+LacV7Jje+cS6Lg59vv47etxPvAeIxjo7Dz8SvSTUREBBEREfw/e/cdH1WVNnD8d+/MZNJ7JxBqEgi9SBHpVUQQWAVZBMVOUxYbrLv4CmLZta0rCouUVURZQFCkKgkgRQi9B0gggRTSezIz975/TDIkJCFt0vB89zObzJ07556TYO4zpzwnOjoagNDQUP72t78xcuRIwDy0vm7dOmJiYrCx+Qvduq1l8eLF9OzZs0ptAxgwbT5hq94h9uxW4IUqv99aPJsGMuzZWfz8r3/QJLitSEBWDVUKPmbMmMHatWvZvHkzTk5OxMfHA+ZAw87O/B/h+vXr8fLyolmzZpw+fZo5c+YwduxYhg0bZv3aC4IgWJFW64S313C8vYZXeG5q6kFSUvahqmqNPvm+8sorvP7660ycOBGADh06cO3aNZYsWVLl4APAo/9HZJz9Cfv9/8HY41W0th5ln1hqKKYcsnn+SHkj9AEBAbz77ru0bt0agNWrVzNmzBiOHz9OaGgoQUFBfPbZZ7Rs2ZLc3Fw++ugjhg0bxuXLl/Hy8qpS23Iz0wDwbtW/Su+rDW37DuDmpfPsWbUM7xat8GsdXN9ValSqtNpl6dKlpKenM2DAAPz8/CyP7777znJOXFwcU6ZMISQkhNmzZzNlyhS+/fZbq1dcEAShPnl5DiY39zppaUdqVE5OTk6JIQUwrxJUlIqTa5VH88Dr6PON5P+7A1nRW2tUv4qMHj2aBx98kKCgIIKCgli8eDGOjo4cOmQe+nn88ccZMmQILVu2JDQ0lA8//JCMjAxOnTpV5Wud2refHWcu8cSSVwvn5JgfxZdLZ2VlMXPmTAICArCzs6Nt27alNpizlv5Tnsa7eSt+/OhdcjPFSs2qqFLwoapqmY9p06ZZzpk9ezYxMTEUFBRw7do13n77bWxsGt7Ma0EQhJpwd38AJ8dQLkW+hcmUV+1yRo8ezeLFi9m6dSvR0dFs2rSJDz/8kEceeaTaZTp0mE7WuPfQGA3YrZlMzsllLFy4sMQN+86bdrnZqYs6RiqxNMFkMrFu3Tqys7Pp3bt3qdcLCgpYtmwZLi4udOrUqUptuh6XRW6iOWBp1bIVcXFxlsfp06ct57388sts376dr7/+mvPnz/Pyyy8za9YsNm/eXKXrVYZWp+Ohl1/DkJ/Pz//6B4pisvo17lX31paJgiAIdUSSZEJCFpGVdYHExOr3LvzrX/9iwoQJvPjii7Rt25Z58+bx3HPP1XienGPH57GZcQpFq8F+0yvkXv6B0NDQMm/aqqoyduxYrl69yubNmzl+/DiBgYEMGTKE7Ly8wnPKv9bp06dxdHREr9fz/PPPs2nTphJ75/z00084Ojpia2vLRx99xK5du/D09Kx0Wy5cSWHsp/uIcOkCQG7KLWJuJOHp4YGPj0+J4ZuDBw8ydepUBgwYQPPmzXn22Wfp1KkTR48ercqPr9KcPb0ZNWse0aeOc1DsgFtpNcpwWhtEhlNBEBqLW7d2c+r0c/S872ccHRvmmH9BRhTZP07lk+WH2XgZjh79DRvfkqsP75adesETE5jrso6sZ87i2KTsvBYFBQVcv36dtLQ0NmzYwH/+8x/Cw8MtAUh2djZxcXEkJSWxfPlyfv31Vw4fPoy3t3eF9d+4+yqv7zqPnSSxcEAgHy14kkMnj2Gn06LRyAS6uzKsQ1vcnJ0xSRo2nLiIbK9n8+bN+Pv7ExYWxujRo3lq0hQcHd0J6dGTsQ8PwdnRvoY/2ZIOb/qe/evW8PDc+bTp2ceqZTcWtb7UVhAEQYD0jBPo9b4NNvAAsHFugc3kveTu6c+VA3sJDOmNrVsAPXv25J133qFly5Z3zU79+9kL0Ie7DrvY2NhYJpx2796dI0eO8Mknn/Dll18C4ODgQOvWrWndujW9evWiTZs2rFixgjfeeKPC+n8WdhlPrZYt8/rh6WaHrfoWKWnp3LqVwfXrMfz04waW7jvKgjfewpCZzWMFOXx7MY6AgAC0Wi2yLDP0/v4EZsRQkBXPrc0n+fzHlQx7bTFdO1tvZ+P7xv6JxKgrbPv8I9ybBOAR0MxqZd+LRPAhCIJQTUZjOja6claTNDADxr9OqM01uqhJRHUcyYffnaNPnz6cPXuWkJAQAgMDeeONN0pkp46PjyehScW9E3dSVbVE8kiArDwjiw9dJcto4lZOAT+cv0HcrvO384TcTgdSlEIWSZVI00nk5pvwdDOvqCxawlvk7bdep1WrVuj0Cq+9sYDh/Qdz81okT/btjpu9HVdvpbBt36949e/Lip2/8u2mXVxb/yWbly+j678/tpSTFJvI+kXvYjLkmStQ4aBA8VVC5nMVxYAhL5e1b/6dZ/71BbaOIodVeUTwIQiCUE02Ok/y8uNqvNy2LgztHYz2cComWUObSbMZ/ERTWrVqxerVq5k7dy4bNmxg+vTpuLu7W7JTjxw5koKkOHMB5TRv/vz5jBw5kqZNm5KZmcm6desICwtj+/btZGdns3jxYh5++GHeP59BeEEyOVu+J/9WPIm9+rOrINd82y4su/jtvjBLPXm2oL3LPE4HBwc6dOhAZGQkubm57Dm4j9kvvY7ftWMAuLbuTGyBxHXMvSCTxw/nw0vn0Z34hfTMbFyczLutx567Qk7qJexcQrB1dDQ3uVK/06JaF6XVb0pWqoadX53joZmdkeWG/e+ivojgQxAEoZrc3HoSFf0pKam/4eHet76rUyZVUUjfNBbnM+EApHd7CHfXEADLTRvKz07dzscLuFrujTghIYEpU6YQFxeHi4sLHTt2ZPv27QwdOpS8vDwuXLjA6tWruXnrFrKzC8N69uL/fttPjx49KlX/tqd/wdXPsdzX8/PzOX/+PA888AAGgwGDwcDggb0ZPnwhqgoajUzsc88RFRVleU/GzevYaG1xcridJE6jM98Ohz//DK261mwY7fq5ZH7610kOb75C70da16ise5VY7SIIglBNrq49cXbuRNTVj8tNwlWRJUuW0KNHD5ycnPD29mbs2LFcvHixxDnTpk0rtUy2V69eFZatGHLIWN0L19PhpPr5kPXnlbiNXAPcvmn7+fmVeI+LiwteXl6W7NQjepknp0rlfIJfsWIF0dHR5Ofnk5iYyO7duxk6dChgnkOyceNGbty4QcimQ7zwzV62bf2x0oHHvyKukZuSxwNBt1ezzJs3j/DwcKKiojh8+DATJkwgIyODqVOn4uzsTP/+/XnllVfYu3cv169fY9WqVaxZs6bE0mX3oPbojbm8N+dlyzGNVgOAqcBQqbrdTbN2HvQe15pjO65z7rebNS7vXiSCD0EQhGqSJImWLV4mPeM4cXHrq1VGeHg4M2bM4NChQ+zatQuj0ciwYcPIzs4ucd6IESNKLJMta6POO6VuGIXLtYuk9JvCkksPE3HDg+hr10vdtMGcnTosLMyy3Hbo0KGMHTuW/l3bF7a1hgsjb0/mqNDNnHzaLNrJP9efwdXXgbf6tbG8Fhsby6RJkwgODmbcuHHY2Nhw6NAhAgPNm/atW7eOHj16MHnyZNq1a8e7777L4sWLef755y1lzJr5JFl2HtgkXuG7bzcBoLXRAWA0GmvWzkKdhzSlfb8m7Pn6ApFHE6xS5r1EDLsIgiDUgIfHAzSxfYLsVblEseH2CyUmM9zRa1Ds+dL7n4frEly/jITEW6GP0n3rVn6a+Sk9A803/qwTMZjyssld+nthiRpy22Tg/ufyN59Tjfm4XjpGgU7GfdBnxC6byKRJk0hKSsLLy4tevXqVuGnHxcUxd+5cEhIS8PPz44knnuDNN98kZdf3AEgaK9wuKhm/nE/JxpBloGU7D9b/qSt2Og1Llixh/vz5zJkzh5s3zb0JkiSxceNGNm7cWOL977//PitXriy3fEmSmLd0GR889zTRm1cT3bu7ZdjFZLBOojBJkug3MYiCfCO7V55Db6+lWbvGMTm5LojgQxAEoYYCvKeSlhGDsWkSqlOxbKfFdp+3xBuF29bfuXu9eQt7lcz4VABcQ3XQNMe89UqYiUNHTtPto0k4OzjQs2kH5uU8gWK/HNAgIRc+NFD4vcuxA7hqIXvce9hg7hG4m9mzZzN79uxSx9XCrJ2SRlPln0upsioZfBQYzanln+7aFA87G44cOcKyZcvo2LFjifPi4uJKPN+2bRvTp09n/PjxFV7DwU5Ps0EPk7ptNUcjTtM9yNy7YjTUfNiliCRLDHqiLfnZRrZ9eYYxL3XGt4WL1cpvzETwIQiCUEMOLQJJt72Je3BXnIcEVrscVVWZPWYMffv2Zfi8eZbjf1LhyVdeITAwkKioKF5/6SX+vGohP8mfo9fpit6NKpl3pNUZjLjarjLXLXBi9RsGFOQULpmVajZKn2onc6qSQxoFJnPwEZOZz/4rN5k8cRIL3v+Yz//xPnGZeUSlZOPjpC+RHh5g8+bNDBw4kJYtW1bqOsOH92fNznWc2/Itvd74PwAUK/V8FNFoZIY/254fPznBT5+dZNxfuuHu72DVazRGIvgQBEGoIUkjYdvKldwLKTUKPmbOnMmpU6fYv39/ieOPPfaY5fv27dvTvXt3AgMDOd0zl3HjRt5ZDKrJhOltNzSkYli3EM0zH1a7TsmxmQQCqlLzKYJRmsptlmdf2MuydPN53t76IbJ7e96J0BIfl8FVUxyH3w9D767n4qtDLO9JSEhg69atrF69utL1aeLnTW5gV1yuHuTg2csAGI3W359FZ6PhwRc78sOHx9jy6QnGvdIVZw+7it94DxPBhyAIghXYd/Um+b/nyY9KR1+NrvVZs2axZcsW9u7dS0BA2WnMi/j5+REYGGhZJnsnSaNB/ksEpn92wfbGCgxHH0TXfUiZ51bEq6k9nAONvma3C68chT5ULunW4GZuLJrShd2bN7Az9wYvf7kZjY2eD7fa4tPUBcfWbly7nMr9X+xHVUFF5cbOtag2dnyU6MXHn+1FpXAzVDDnDEO1DPtYvqKiV5vzGAe5/v0nAByOSaZnjVpaNlsHHaNnd2bjBxH8+OlJxr/SDVtHXcVvvEeJ1S6CIAhWYNvWA5umTqT+7xKm7MrPG1BVlZkzZ7Jx40Z+/fVXWrRoUeF7kpOTiYmJKbVMtjjJyQNmH8ekuqL7aTzGqxfLPfdutFrzxJSazvmQTCrRmbmVOleWZfo7m9j2ySK2bfiOV/oEM7d7c1p7OPBAoDt/6d8KJx970nIKyMgzkJlnJOHAVly7DiFf0pGvKBgUBZMKCub5NpIsI2tlNFoZnU5GZyOjt9EiBTbjh8Dx3HTw40xQFz5u6cXkzadq1NbyOLjoGT27M1lp+ZzaE1Mr12gsRM+HIAiCFUiyhNtjwST88yjJq8/i/WLnSr1vxowZrF27ls2bN+Pk5ER8fDxgzrdhZ2dHVlYWCxcuZPz48fj5+REdHc38+fPx9PQskbuiLBp3D4xTd2BaMwzWjCLHFIKi9UHncAvajsdm5BQkuYLPoIp5noYk1Sz48FNkIl1UFEWtVNbPiIgIEhMT6datm+WYyWRi7969fPbZZ+Tn56MpDIj27dtHv8QYftn5I506dapG7R4AnuTMjXS2XYriFzsFQ4EJnU3NJ9neydXbnuD7fDi3/ybdR7X4w2ZAFT0fgiAIVqL1sEXjogdN5f+0Ll26lPT0dAYMGICfn5/l8d133wHmDd5Onz7NmDFjCAoKYurUqQQFBXHw4EGcnJwqrlPLEJSHVmFy6IBOuoaj+j/0WeHoj8wm598LMMVeuXsBauE8DblmN+JMVJoZpErfbAcPHszp06c5ceKE5dG9e3cmT57MiRMnLIEHmBOddevWrZqBx23tm7gwOtNc7rJfKvi51EBIbz+y0wuIv5JWa9do6ETPhyAIgpUYYrMwpeXjNr5NxScXqigzqp2dHTt27KhRvXTdB0H3Qebr5eeB1gbDZ4/hkPw5yvIV5AS+hJx/A7n7OGx6DL6jggqqKkENP6ErgJ+28nMcnJycaN++fYljDg4OeHh4lDiekZHB+vXr+ec//1mj+hWJNBgAmV+UPJ43KWiqEEhWlk9zZxxc9Vw+moh/Gzerl98YiJ4PQRAEK8m/lgFaGX3LhpvLQdLbImlkdLPWYXx4PYrOG/vr72Gb8DW6nyZiSkou+QZVQS3KS1IDqgS1sffeunXrUFWVSZPKT7hWFU+2MKdyP2Cv0GTvKf4Zbv0eEEmWaNvHj3O/xZGRVLl5MPcaEXwIgiBYiZJrRLbXItXCp2Vrk2QN2q7D0Lx6FOO03zEM/BJJKkDJLJnWXVJNqMg1jhxUyt0Yt9LCwsL4+OOPSxx79tlnycnJwcXFOgHf1K5NudmvI23TzMNNHyiZfLzP+gFIl2HNsHXU8dv/Llu97MZADLsIgiBYiazXoGQbUFW1ktuxmy1cuJC33nqrxDEfHx/L5FNVVXnrrbdYtmwZqamp9OzZk3//+9+Ehobetdz5a+eyP/93sPRdSEgqZfZj3J+dxiIg05iJe/EXVAUFGa0Vei0ay9RKWSOz55GufH4wmv/LS+OzrHReqsT7iqeAvzNIupONrZY+41qx66tz3LqeiVeziufv3EtE8CEIgmAFqqKSHZGATVOnKgUeRUJDQ9m9e7flefEJle+//z4ffvghq1atIigoiEWLFjF06FAuXrx410mnx3JOEiD7cp9DV0t6dxW1ZBRQWFcP9SgkXyPdLrtE8KGaTKDK1WpTcdbo+ahL2QYj/5eXBkBHQ8WTbctLAX83rbt5E/bNRa6dTRbBhyAIglB1185eZKPxByRbG6SN21BUEyZVKfZVQaHkVxMKCib2nT7E9cxrDN0xlMHavmgkTWGCLBVFUfjPkv/S+cEOhOXsYM+J7bgNsyf5x2RGzx5Ju0HBxRJoqcX+B7c0qYxwG8ZLY1+rsP5XDn8GMb+UWnqrKiYUK4zQq1LjCj6S081p5fUm2Pjw3VfRZGVlMXnyZJYvX86iRYsqfY30W7kY8k04udvWqK6NkQg+BEEQrODr2HV85/0DriYn5DQZ8/8k81dVRlPiiGR5LiOTYUgnKz6bCy9d4Kr2Kp4t3Ok8LhRHbyeyErPJSc/BLdiZ9IJ0igZNfIK8iL10g5YPBCIVbiuHJFH0PxnoKXVhYNDgCmpeqHDVjXTn1rNK4ZyPGmpsPR9xt8xzX+xyK063PmPGDEaNGsWQIUMqHXyk3Mxm25encXK3pVVXrxrVtTESwYcgCEINmRQTv+UcZkyrMSzqW/lPvkW2+W8jZ2oOQUFBJCQksGjRIg5/fIKzZ89y8eJFtrCNZc+twt/f3/KeZw89y7Vr1/jqqW+s0oaiYZk7AwTzrraNKWywjp5tvSH+JmmOGtKzCnBxtCnzvHXr1nHs2DGOHDlS6bIjjybwy6rzOHva8vCczmh11k9m1tCJ4EMQBKGG9t/YT2xWLO/1e69a7x858vbmcB06dKB37960atWK1atX06tXL4BScy6qOqm1IkVbvkl37l6rKKg1zG5aVL5cG2tta9GwHC077Y18fz6eZ3o0K/V6TEwMc+bMYefOndjaVm7oRDEphH1zkeYdPBjyVLs/ZOABIvgQBEGotjxjHmGxYSw7tYwQ9xA6elV+suHdODg40KFDByIjIxk7diwA8fHxJfZySUxMxMfHxyrXK066Y9RFVYyoqkTclTQURTVv0qaohH97ibSEnFLvH/5sKCBxe/TG/I1DmoFzdka+Ox9HyVeKfy22+Vvx4yqWDeTuVDxJW3np2opvJFf8mIoEqEiyhCxJaGQJjSQhy6CRJPo2c2XXtTgWZyXwlCmgVMKxqqSAL5IQlUFBrpHOw5r9YQMPEMGHIAhCleWb8vnvuf/y33P/JSUvhY6eHZnbfa71ys/P5/z58zzwwAO0aNECX19fdu3aRZcuXQAoKCggPDyc996rXk9LWYpu0Hf2TeSk5eGqymz84FiZ72vZ2Txf4eqJWwDsWHa2zPMmAb/pDbwWccsa1a0zRfvwfqa/zJxhQSVeK0oBX9yTTz5JSEgIr732WqnAA+D6uRT0Dlq8A51rq8qNggg+BEEQquBq+lXm7pnLtcxrPNL6EZ5o9wTNXZrXqMx58+YxevRomjVrRmJiIosWLSIjI4OpU6ciSRIvvfQS77zzDm3atKFNmza888472Nvb8/jjj1unUYBSGH00WTYIRZXJUP1J9xuLa+5xtFIBPce0NA+baMzDJ5IEASHueDRxBCAzJY/r55LN02GLRTBS4f/9uPYCLVztePyJkJIrfe/4fwnz6l/JMgel6H+Fx6QyVwqXzF1SxvwVqcTLxc5Wzb0tJkXFpIJRUVBUFZMCJlUlKS2X/9t0Frf80j+zyqaALy7mfApNQ9z/sBvKFRHBhyAIQiVdTr3M9J3TcdW78t1D3xHkFlTxmyohNjaWSZMmkZSUhJeXF7169eLQoUMEBgYC8Oqrr5Kbm8uLL75oSTK2c+fOSm0sV1lpBj+yTO6k6kPR+nVAl3yKwITPAEjRBNF9ZPO7vt/J3ZbQvk3KfX3zdxdw0et4qJW31epcF2ISsvg/ary1DQAmk8KtmEyC7rP+cFljI4IPQRCESsg15vJy2Mt42Hnw1bCvcLV1tVrZ69atu+vrkiSxcOFCFi5caLVr3ilL78fqWysYNK85bVu3BFUl/Xg4RskO56CyP8VXhUrt7O1S24p6hCo7WTYsLKzc19ITclGMKh7+jtaoWqMmgg9BEIRK+P7i98RmxrJhzAarBh716UZiPF8v/QXVIGOX4o4WHVpd4aRKScKl6wDrXUwF+2u5LHo17PZEUklCkmHg2Nb07Vl+r0l9UgqXAVmj5yM3swAAvUPld/e9VzX83Y8EQRAagP9d+h8jWoygpUvL+q6K1Zy/fBXHOD9Uk0puswRy290gsEntBAG+3b3I8NKhtdGgs9Gg02nQa2Vsskzs/+9F0rMKrHatJUuWWObKFMnKymLmzJkEBARgZ2dH27ZtWbp0aYVlmarY83E33s2d0WhlblxMrXFZjZ3o+RAEQahAWl4a0RnRvNDphfquilUV5Ql5+OkehLZqU6vXevKJDmUeP3M+iV8/OcnSL47x+rxeNb5OeXusvPzyy+zZs4evv/6a5s2bs3PnTl588UX8/f0ZM2ZMueUpivWCD51eg19rF66fS6bT4KY1Lq8xEz0fgiAIFUjJTwHAy/7eSoNdFHwUz5VR19q39cSuvRt2l7M5ciaxRmUV32PFzc2txGsHDx5k6tSpDBgwgObNm/Pss8/SqVMnjh49etcyi3o+NFaar9Is1IObl9IwFlSctv1eJno+BEEQKuBh64F3qsrlXzaB62lUVUFVVPNXVUVVTObnKEgGEzZHz1HQ0h+TuxMaVS5czmk+h8L3oigoipFMmyTS2nsCqmVGZuFOLSX2apEK15lK0h3PixaOWo5TuLOL+f3ZWRews/VHp3MrLPv2MtPoxFygC9dPXUBJuImTuwst2nap058twLg/t2Pd6we4ciaJHu2rvxrmbnus9O3bly1btvDUU0/h7+9PWFgYly5d4pNPPrlrmdbs+QBo1s6dAxsuE3clnaZt3St+wz1KBB+CIAgVcLZxZtE3Kq6Zm6xetg8w5S8a8m3qfimIS643j0kdidrpSBQKkMyT/0jH3tGlTuvhUDgB06RUvwemoj1WPv30U5555hkCAgLQarXIssx//vMf+vbte9dyb8/5qHbVSnD3d8DOSceNS6ki+BAEQRDKJ0kSXs1CUDxcsH35eWRJRpI1SJKELGuRZAlJkpEljTlhd2oasoszsp09iqqALJnPl2XzQzJ/f+vHz8l/ey27HlyHrXczJCQUVSnsUVEK04EX9q6oqjn9OIqlxwXM5yiF+cfVwqTkRe9VVYW8/Hi0Widk2Y7iCcaLhlq0E3TY48C5349xfIszxoIysmnVMpvCtOVpexNY9HsCigSKJDFscgh9uvlV8O7K7bHy6aefcujQIbZs2UJgYCB79+7lxRdfxM/PjyFDhpRbdtGIlLV6PiRJwr+NGzcuplmlvMZKBB+CIAiVYNeqFYbYGzRre1/FJwdWrsx0ByfyARvZBjtdbaXbbleps+wdLwLVm/+xcOFC3nrrrRLHfHx8iI+Px2Aw8Ne//pWff/6Zq1ev4uLiwpAhQ3j33Xctu/TKsoS2mzuZt3KxVUFSVWxj87h4NqlSwUdFe6ykp6czf/58Nm3axKhRowDo2LEjJ06c4B//+Mddgw+TYt2eDwA3P3uuHEskN7MAO6eyd8u914ngQxAEoRK07h7knT5j1TIljflPsGqo+96G0orurtUb+ggNDWX37t2W50X7muTk5HDs2DHefPNNOnXqRGpqKi+99BIPP/xwicmezz3TuUR5/3r+F8t8i4pUtMeKyWTCYDAgyyXXWGg0GpSiRB7lsOZS2yJeTc2ZaU+H3+C+h1pYrdzGRAQfgiAIlaBxdcWUllbq+N0+9QNs3LiRL7/8koiICJKTkzl+/DidO3cGQNKa5zqoRkOt1r0ypMKP9ko1V75otVp8fX1LHXdxcWHXrl0ljv3rX//ivvvu4/r16zRrVnqregAFSDt8i/eP/AqY91xRJXBVZAqctbz8fj/LuZXZY6V///688sor2NnZERgYSHh4OGvWrOHDDz+8a7t+P2VegaOxYvDRopMnTh62ZKc3hKCzflRpqe2SJUvo0aMHTk5OeHt7M3bsWC5evFjinOomchEEQWjING5umNLTUQ2lA4XQ0FDi4uIsj+KfwrOzs7n//vt59913S71PLur5MN09+KjM315Jksp8fPDBB5Vqn2UNjHr3noDyREZG4u/vT4sWLZg4cSJXr14t99z09HQkScLV1bXcc7J99diqEoqvHpvWTuQ3scNVMd+yTLnGKtdv3bp19OjRg8mTJ9OuXTveffddFi9ezPPPP3/X991KzQWgZ/fSgVV1SZJEy85eXD1+i/yc+g8860OVej7Cw8OZMWMGPXr0wGg0smDBAoYNG8a5c+dwcHAAqp/IRRAEoSGzbdcWFIXcU6ewLza3AMr/1A8wZcoUAKKjo0u9drvn4+7ZPSvztzcuLq7Ee7Zt28b06dMZP358pdp3e9Sl6j0fPXv2ZM2aNQQFBZGQkMCiRYvo06cPZ8+excPDo8S5eXl5vP766zz++OM4O5c9z+XqtXRc4s0/E9u4fFqN9ePBQc1ZtGAvnukKD83oVGGd7txjxdfXl5UrV1a5bQB6FZyc9NV6b3k6D2nKuf03Obz5Kv0mBVu17MagSsHH9u3bSzxfuXIl3t7eRERE0K+fuQuseCIXgGeffZYvv/ySo0ePiuBDEIRGy7ZdO2RnZ3KOHCkVfBR96tfr9fTs2ZN33nmHli0rTsNeNOdDqSD4qMzf3juDn82bNzNw4MBK1aO46gy7jBw50vJ9hw4d6N27N61atWL16tXMnTvX8prBYGDixIkoisLnn39ebnk+3vak68ElH3QKXN8YzRcbo/EEkh0lQkI8yn2vtUlSdWfB3J2jmy09H27J/v9FEtzLD58WtTXhuGGqUYbT9PR0ANzdb69VLkrkcuPGDVRVZc+ePVy6dInhw4eXWUZ+fj4ZGRklHoIgCA2NpNFgExhIQfS1EseLPvXv2LGD5cuXEx8fT58+fUhOTq64TK15pYNqqtowQll/e4tLSEhg69atTJ8+vdJlFmU7rU7Px50cHBzo0KEDkZGRlmMGg4FHH32UqKgodu3aVW6vB4CDnY55H/RH07V0++b+393zcjQmHQY0wTPAkbC1F1BM1RvuaqyqHXyoqsrcuXPp27dviYk+n376Ke3atSMgIAAbGxtGjBjB559/Xm4ilyVLluDi4mJ5NG36x853LwhCw6QqCobYWLQ+PiWOjxw5kvHjx9OhQweGDBnC1q1bAVi9enWFZVZ22KVEPcr521vc6tWrcXJyYty4cZUu15qp1vPz8zl//jx+fuZlskWBR2RkJLt37y41FFMWGxsNzz3TidwW9mTa3p7sGZ+cU+P6VcXtfLDWJ2tkBkwOISk2i1N7YmvtOg1RtYOPmTNncurUKb799tsSx4sncomIiOCf//wnL774YoklWMW98cYbpKenWx4xMTHVrZIgCEKtyb9wAVNqKg59+tz1vLI+9ZfHstS2CqtdyvvbW9xXX33F5MmTy024VXZlKn/qnebNm0d4eDhRUVEcPnyYCRMmkJGRwdSpUzEajUyYMIGjR4/yzTffYDKZiI+PJz4+noKCuwddkiQx77VevP7xQDyHmXOC/Lj4KKtWn77r+xoTn+bOdOgfwOEfo8hMyavv6tSZai21nTVrFlu2bGHv3r0EBARYjufm5lY5kYter0evt+5EHkEQBGvL/v13JFtb7Lp0vut5RZ/6H3jggQrLlHXmv33xixZxK2Q1zg8Mwu+hGZZVMHcq729vcfv27ePixYt89913FV6/BEkGFNQK8l6UJTY2lkmTJpGUlISXlxe9evXi0KFDBAYGEh0dzZYtWwAsS4yL7NmzxzI/sCKPjQsh4f4A/vf338k+eIsfm0UzemDzKte1qmprzkdxPce05MrxRPZ9d4kHX+hY8RvuAVUKPlRVZdasWWzatImwsDBatCiZHMVgMFQ7kYsgCEJDlnvyJLahocg2JTNSzps3j9GjR9OsWTMSExNZtGiR5VM/QEpKCtevX+fmzZsAliWyvr6+eLXtS9ITPVEuRWH6/QoZP10i9fvvCFm+Da397f1VKvrbW9yKFSvo1q0bnTpVvCKkuKLhBZWq/61et25dua81b97carvm+vg4Ymhujy46h+vfXWXBjmgWLel/e75KI6W30/LAo0HsWH6GqJO3aNHp3to9uSxVGnaZMWMGX3/9NWvXrsXJycnSdZaba14H7ezsbEnkEhYWRlRUFKtWrWLNmjU88sgjtdIAQRCEupB78iR2ZdzQiz71BwcHM27cOGxsbCyf+gG2bNlCly5dLL3BEydOpEuXLnzxxRdobGxpM38VoavCab/3DM7vv4h8KpUri58ucY2K/vYWycjIYP369Tz9dMn3V0bRh8bq9HzUpRnz7qPZI+afrX+aQoGhdremr6vAplVXL5qFerB33SUK8qqex6TRUasAKPOxcuVKyzlxcXHqtGnTVH9/f9XW1lYNDg5W//nPf6qKolTqGunp6SqgpqenV6VqgiAItUYpKFDPBYeoqevX1/q1zs17WD3dp12JY5X526uqqvrll1+qdnZ2alpaWtWv+/tv6mfP/aLGXbtQk+rXmQ/+cUj98Pndatyt7Fq9zttfHVNbv/pTrV6jSPqtHPWLmXvUvd9drJPrWVtV7t9VHnapSE0SuQiCIDRESr55YqRka1fr17IJCCA/41KJY5X52wvmvErPPvtsta4rSYU9H9XMcFrXHhzdmj0fnuTLtw7ywsLeeHvY19q1KjtotGTJEjZu3MiFCxews7OjT58+vPfeewQH304ilpCQwGuvvcbOnTtJTUujc9deTHpmAcmKG0ajQqytkf2/ReHdxZPgNmUvpb4XiL1dBEEQKiA72CPZ2WG8dav2r+XoiGQAJT8fuQ4n40ty4wo+2gV5sAfwNEisX3AIABWVNuNbMnxo/WzWVlEmWlVVGTt2LDqdjudf+xdfnknizJEfmDt7EoFPLUVvY4skS+TbqizYdo71re9v9PNZyiOCD0EQhApIkoRtcDC5p07W+rU09o4AGPMysNHX4cRDS89Hba/tsJ4ZXwziwpVUtq+7gC4mFwmJyxuiuHzqFjP+cl+Ny883mPj+agJGCV7852+lXlfBskRZBZoPe5NDKXBgRxoAbj2f5/rW0Tz66ir8WncmLfE6hw4dYswra/guzg5v96as+OhzHnukG6+GJPPcc+Zeqz0XEnly1RF+Ph3PqI5+NW5HQySCD0EQhEpw6NOHlP/+FyU7G7lwP5XaoHM1JzHLvPI7Hl1H1dp17iRb7qK1O4HT2kJauRGyoDcAJpPCR7P2YBeZxflLKbQNqtmwxcGryWQYTEjAoeQMJPV2L4R0x9c7j0kq5KWad8SNzdOQkpBJzq00AGLSDTT1tuGfU7sQ2toDezs9Bw8esAQfA0O8GdLWh0VbzzEwxAt7m3vvVn3vtUgQBKEWuE4YT9KyZaR8sxbPZ5+ptet49BtPisvHxD89D8NHt/DtP63WrlWC9bKr1xuNRubpJffzzWsH+N/yU7y25AFstJpqlycX/kzefKgdT/Wt2lCOqqqMGTMGp7592bfCvHOuwWCgzS/v0Sp1O1+++yUODg68++67xMfHl9oY8G8PtWPIR+F89utlXh0RUu02NFQ12ttFEAThj0Ln74/7n/9M0r//Tf7VqFq7jo2DB97/WIicA7nn6i6T5+2pBY37tuDqYovLYD/cMxWWzwyvUVm2OnPgUmCs+jyYsjLR6nQ6NmzYwKVLl3B3d8fe3p6wsDBGjhyJRlMySGrmYc8L/VuxfN9VopOya9SOhqhx/ysTBEGoQ15zZqPz9eXmG6+j5OfX2nVufbMMxUUiYMqbtXaNO6mSucujMfd8FHl8/O3VJZHRadUuR6813yLzTVUbiirKRLtnz55SmWi7devGiRMnSEtLIy4uju3bt5OcnFxm4rgXBrTCy1HPe9svVLsNDZUIPgRBECpJtrPD/713yT9/gZvz5tVKAJJyfAeEx2L3xHB0jq5WL788kiXqaPyrK2RZxmOoeS+YH96LqHaGbWdb88Z//z14rYIzzVRVZebMmWzcuJFff/31rploXVxc8PLyIjIykqNHjzJmzJhS59jqNMwY1JodZ+OJT7+39n0RwYcgCEIV2HXuTJOPPyZr7z6uTf4zhvh4q5WtKAo3334TxUdL4PRFViu3Ugp7PlAbf/AB8Ni4YNI1KvaqxLJl1Vul1MLLvPJIruRy18pkol2/fj1hYWFcvXqVzZs3M3ToUMaOHcuwYcPKLHN0J39stDIbj99bu96K4EMQBKGKnAYNJHDtNxhTkol+9DEKrl+3SrnRy15Bcy4Tt788j8a29lbUlO0eGG8pRpIkXv14AACmE6nVLsfTwabSP5mlS5eSnp7OgAED8PPzszyKb/IXFxfHlClTCAkJYfbs2UyZMuWuOxQ72+oYEerL/yJiG9Uy6IqI4EMQBKEa7EJDaf7ttxgTE8nctbtaZRhzM4jfuYILb/6J0yM7kf/xzzCyFU1Gz7RybSsmVZDnY8mSJfTo0QMnJye8vb0ZO3asZZO8IgsXLiQkJAQHBwfc3NwYMmQIhw8frvW6l0eruz2JMzWjmkNkkoSqVO6mr6pqmY9p06ZZzpk9ezYxMTEUFBRw7do13n77bWzu2KzwTn/q3pSrt7I5dj2tem1ogMRSW0EQhGrKijwOQNLvm0iK2Q2KAiaTeXM2k4KqmMCkmI9LEv4vLMAluA9XV71G7ta9SBfSzNlMnSQ0Hf1wnDoU3/Fz2H7sZw5eSceoQMTOXRzbtZv0pFuggkdAAL0feYQWnTqiAtnpaez79juunTlDfk4OTYKDGPTEn3HzMecLUS1bwUDRfI6iHWyRzC+pqGhScmhOW8K+jkOvzynV1u9Wb6F3h5E81vsvKIqRdbu/oG/PAbw9bQ2mPA2eTR1JPqdhQu+ZeD/YhAJDPj8f/JaB/Qfz8csbcHZwK1WmopjIMqRi53SXW5F0xywUydyrUfIBSBKyLCFrJKTCrxqNTL6Tgj5T5i+L99BjcLal/ZJU+HMoLNw8tKKayys8LgHJWRqaOsWyfl80EgpIKjIqWo2OwV0m4VAHPVS9W3rQxNWOH47foFtg6Z9jYySpDawfJyMjAxcXF9LT03F2dq7v6giCIJTr/PpnMf1zLxIyqiyZ+5Jlqdij8LlGQhOVj9rbH9fxj5A+798Ym8jYPtgH94GP4NZpOFcTIvl6/35+vqAnMccdJ5ssbDUFpF86DpKMrbs3kgRJJ/YT99vPdHzh/7Dz9ufM8kXIsobAkRPR6u24+dsOUiNP02XOEjQ2+lLTR8v7g38rw4MHs21oiYyuEp3imblpvPbVOF4a/RGt/Tsia0teSQJyC7KZu3w0cx7+ByFNu95RgoRJNWCU8pBlGVkq55rq7fNvH1JLNMRyFytKOapKhQnBJCTV3PuxziGfGF31Jp72D/iNJ9p9V/oF9/cZ3Hl8tcqsqnnrT3LwSjLbX3oAp8KJsA1NVe7fIvgQBEGopt8O9MfLcwhBQRUviT3/2hjYbN4wztjWnrZr95CtqPxwJIyNxxM5leCLTmOkf8ssJvfuRL+QDshy2RMd3d3d+eCDD3jggQcIDg7mzJkzhIaGAmAymfD29ua9997j6aefrnRbwiJuMm39cb6e0JW+3StO6X358mXatGnD6dOnad++fanXCwoK+PTTT1m0aBGXL1/G09Oz9DV/PkzY79uY9eJLeHi7VrquVfHztu7o9akMHnTFckxRFPOQSGEEoygKKmqJY6pi/l5RDNhoTOh0joCMJMkkZ6Zw8mgvjM5vM7z747VS7ztdT87hwU/3Ma5rE/5vTOmfd0NQlfu3GHYRBEGoBkUxkpcXi6NjcMUnA35PvkqcaQn6+4dy1K0573z1PRE3PChQbGjrqWX+UHiszzCc7crvxjeZTKxfv57s7Gx69+5NfuFSX1tbW8s5Go0GGxsb9u/fX6Xgo+hzqFyJmYCqqjJ37lz69u1bKvD46aefmDhxIjk5Ofj5+bFr164yAw9zOVT6mtWRmhqFXl96sql85wWrmARVozHfOpU6TEXfzMOeKb0DWff7df72UDu0msY9ZVMEH4IgCDWgVrAWQlVV4tNS2ZWQws8txhJxxBODoqWVm5Yn78th/H0dCPK/+x4up0+fpnfv3uTl5eHo6MimTZto164dBoOBwMBA3njjDb780pyu+8MPPywzXXdFilJhlNfbUlxR9s79+/eXem3gwIGcOHGCpKQkli9fzqOPPsrhw4fx9vYude6NBHP+jLxayJeSnBLFiRNDAGjZcqVVy9bK5ltnYvotjly5gkajQSNpkGUZxSShQYMkgVYjIyGh1UhoZLlwNO72PBVZklAUBUUxYavXm1+TzeeC+XehKGBSVWx1WgYHe7E07DJHolPp3crDqm2qayL4EARBqAZZ1qLX+5GbY76BZuemc/7mFS7FxXE5IZ3o5Hyup2m4keFArtEWkGntpuHpnjmMv68nrf0qv2lccHCwJSvmhg0bmDp1KuHh4bRr144NGzYwffp03N3d0Wg0DBkyhJEjR1a5PUWJuCpKaVGUvXPv3r2lsncCODg40Lp1a1q3bk2vXr1o06YNK1as4I033ih1rqO9uWtep7PeHIbc3FTCwkdgY5MEQEFBT1o071fp9y9ZsoT58+czZ84cPv74Y8vx8+fP89prrxEeHo6iKAQEGHnzb5+QIf/bck5WlhvHj42irERtYWFhhIeXTPfu4ODAvHnzAPjhhx84ebJkPpImTZqU6r2aZgt7fs2hd6tHK92mhkgEH4IgCNWUp+nPjA12JOd9TXKuC2rhRE0HnY4mTrkEuhbQv5WR1t4q97XpQivf6u1Sa2NjQ+vWrQHo3r07R44c4ZNPPuHLL7+0pOtOT0+noKAALy8vevbsSffu3at0jaLVpJpyxkBUVWXWrFls2rSJsLCwu2bvvPN9+eX0bDjZu5jbp7/7UtOq+HXPKGxtkzAa9YSGriWgSedKv/fIkSMsW7aMjh07ljh+5coV+vbty/Tp03nrrbdwcXFh38Gt+LV0x83dydx7oZrIuXYWUGjfswc6O29UVUVRVUyqytmrMQQEXmf+kk8Kf9Yqkiyj1+nISonH0cOPDl31TH/p9cL3mYfQTFp7IhMzae/vjE4jkXUrltxbl8nPz0ev11vt51bXRPAhCIJQTY7uj3ExNQYfRyNvDIVgX29CmjTHx9WrVq9b1g3dxcV8Iy9K1/32229XqUylMPoor+djxowZrF27ls2bN1uydxZd187OjuzsbBYvXszDDz+Mn58fycnJfP7558TGxvKnP/2p3HZA2QndlyxZwsaNG7lw4QJ2dnb06dOH9957j+DgknNs7uyRaNJE5m9/d8XPd2qVAo+srCwmT57M8uXLWbSoZHbZBQsW8OCDD/L+++9bjrVsOatUGSfyNERwgr5dWuPrW7KeZ/dtJ/KUMy9MGl3m9Y//toc0Jz1zpjxy13qmp6fz8ccf89tvvzFo0KDKNq/BadwzVgRBEOpRr6CO9A/yws3RjacHPkT/0B5WDzzmz5/Pvn37iI6O5vTp0yxYsICwsDAmT54MVD1dd3lUS89H2dFHRdk7NRoNFy5cYPz48QQFBfHQQw9x69Yt9u3bZ1mJU95FpTIinvDwcGbMmMGhQ4fYtWsXRqORYcOGkZ19e4fXoh6JkJAQwsLCOHnyJH/96zvY2Ego6mry8tIq3f4ZM2YwatQohgwZUuK4oihs3bqVoKAghg8fjre3Nz179uSHH34oXYhluXDZ84AiIyPx9/enRYsWTJw4katXr5Z4PSwsDG9vb4KCgnjmmWdITEwsVYaLiwv9+/dn7969XLtWuT1nGiLR8yEIglADswa1ZsIXB9l88gaPdCk9B6KmEhISmDJlCnFxcbi4uNCxY0e2b9/O0KFDAXO67rlz55KQkICfnx9PPPEEb75Z9d1wLXM+yku3UUFWBltbWzZu3Fila1qKLOOa27dvL/F85cqVeHt7ExERQb9+5jkcZfVIeHiMJXzv10Aiv+4ZyrChv6HV3n1YZ926dRw7dowjR46Uei0xMZGsrCzeffddFi1axHvvvcf27dsZN24ce/bsoX///pZzi5K3KUrpVTA9e/ZkzZo1BAUFkZCQwKJFi+jTpw9nz57Fw8ODkSNH8qc//YnAwECioqJ48803GTRoEBEREaWGV/r168eZM2c4fvw4gYGBd21bQyWCD0EQhBro3tydBzv4snjreXq19MDPxc6q5a9YseKur8+ePZvZs2fX+DqWOR/lRR+14Pby3oqvmZ6eDphznMDtHolXX32V4cOHc/z4cVq0aMEbb7zBmDEHOHBgCbCC8L1tCQleQ5Mm95dZbkxMDHPmzGHnzp0lliwXKQrKxowZw8svvwxA586dOXDgAF988UXJ4EMuSlFfOplZ8UnAHTp0oHfv3rRq1YrVq1czd+5cHnvsMcvr7du3p3v37gQGBrJ161bGjRtXoixZlvHy8iqzZ6SxEMMugiAINfT2mPboNDJTVvzeaLc+t8z5qMO7Qsmk73c5r4y8IsV7JEaMGMHOnTt55JFHGDduHHv37uX+++djo5sAwLnz08rtuYmIiCAxMZFu3bqh1WrRarWEh4fz6aefotVq8fDwQKvV0q5duxLva9u2Ldfv2FBQljSF9a04/4eDgwMdOnQgMjKyzNf9/PwIDAws9/VOnTpx8+ZNYmMb5263IvgQBEGoIQ9HPd883ZPMPANv/Xi2vqtTLUX35rLmX9TeRQu/VnDJorwixXd/vbNHonPnzrz++us89NBDfPHFFwA88MB75OU5IMsK5y/8UGbZgwcP5vTp05w4ccLy6N69O5MnT+bEiRPo9Xp69OhRahO9S5culRryKPrZldXzcaf8/HzOnz+Pn1/Z2WSTk5OJiYkp9/WgoCDc3d3Zu3dvhddqiMSwiyAIghW09HJk5qA2zD8VzfntJ9FivhnJmBNoyoXfSxLISIXHCr+XzLMFNNLt82TgwOVkWns5EOBqh0aSyEXlOiaCNTr0RedJRZukmZNSWTZNk0AjSUwN8qOZc+nhhDsV9QyUN+G0Nqh3mXBapLy8Ip6enuX2SBRPfubgMByTaSNxcfOQJB0a2Qkvrxa4uTUDwMnJqVSWVgcHBzw8PCzHX3nlFR577DH69evHwIED2b59Oz/++CNhYWEl3le0M3BZcz7mzZvH6NGjadasGYmJiSxatIiMjAymTp1KVlYWCxcuZPz48fj5+REdHc38+fPx9PTkkUfKXv0iyzKDBg3if//7H1FRUZVe+txQiOBDEATBSsZ18eeV3FQuoGKba0KVbn+4VyVQJYmiPdHUoh1lLfddicL0D+ZIQpKgtSPJwGE1r8QCipOKodJ1OvZ7NpuGVLwXiKLefaltbbjbJNaK8orY2NhUqkdiQP8POHWqFbeSPuDmzTkAXLsuk5Pdmn79/o2bW8sK6/nII4/wxRdfsGTJEmbPnk1wcDAbNmygb9++Jc6T5KJhl9I9H7GxsUyaNImkpCS8vLzo1asXhw4dIjAwkNzcXE6fPs2aNWtIS0vDz8+PgQMH8t133+Hk5FRuvUJDQzlw4AC7du3i6aefrtTcmYZCBB+CIAhW4qDXsbhNE/4aeYMv72vNcE+XapWzcOFC3nrrrRLHvH18OB8TS4bRRAv7snsy/rb4HV58+S/mxFaKSs99ZzFqK7dxiWKZ/Fl+9LF3714++OADIiIiiIuLY9OmTYwdOxYAg8HAX//6V37++WeuXr2Ki4sLQ4YM4d133y136IC7DPVUlFcEKt8j0bHj82RnP0rk5Z/JyswnPWMfjk77OHZ8KD7eX9C+/dAS59/5foCnnnqKp556qtyfDYBE+RNO161bV+777Ozs2LFjx13LLvN6ksSwYcNYtWoVZ8+epUOHDlUuo76I4EMQBMGKnmziyYG0LJ47G81HIc14xMetWuWEhoaye/duy3ONRoO7Tou7Tltq35Zt27Yxffp0pk58DB+728tKzfuHVO56qYU5Mda/9zv2qKiSYn7ICkrh9+ejj5KdaMOg7pP45scP+XnpKWK2OwASufnZbN61mz5txzI2tCU5+Zls3PEFvbYN5LXxS8u8Zp7tLXAFo2JET8nlpEuXmt8zYMCAEsdXrlzJtGnTgMr3SAA4OLjTudOfC59N55dfWwGQkHiI9gwtdX51FA27VGbCqbU0b96coKAgfv31V9q2bYtW2zhu642jloIgCI2ELEn8u20g8y7G8MK5a8TmFTAr0KfK5Wi1Wnx9fct87c7jmzdvZuDAgbRsWXoIQa5wLYlZrrN5Kauh201knc48HmQClMKxI0WiS0gHuigdURX45kcwBaTjPDgXABdJYv6ov1M0e1TCE+eeT7Pwjb9g6ByNp9ftjeVMmDApJpISYiAJ8o35OFByN9+K8ooUqUyPRJntze2Ind0p0tIOoKqqVSba3p5wWuOiqmTw4MEsXbqU48eP06NHj7q9eDWJ4EMQBMHKbDUy/2rbjKa2Niy+GoeNLPFc09K7ut5NUTZMvV5Pz549eeedd8oMLhISEti6dSurV68u9ZpK5Xs+iuaePPbwcIK9yhkmKWbOrImMHv0AY8c+XO45u3fvRpIknnp8As7OzqVeXxu2lkthlzCqxspV0opGPbiRX/e0xtX1Er/uaY2z0xx69KhZvpTb8UvdRh8+Pj507NiR8PBwOnXqhI2N9fbKqS2NZ3aKIAhCIyJJEq+28GVmM2/+fvkmy2NuVfq9Rdkwd+zYwfLly4mPj6dPnz4kJyeXOnf16tU4OTmVSkRlrkTlez4sCb+sNOE0Ly+P119/nccff7zMwMN8rcJhCqWOuwow/37ah26zPI+N/d1qZauV/JlXVa4xl+TcZFLyUkjNSyUtL83y6NqrKzk5ORw8dLBWrm1toudDEAShlkiSxIKWfigqvHn5BgpqpXpAKsqGWdxXX33F5MmTy8zOqVqmQFbMZAk+av6Z1GAwMHHiRBRF4fPPPy/3vKJhClMdzpEozscnCI1mFydPDcXJ+SCKYkKWKzdBt0yFMUdlh4yKLFmyhPnz5zNnzhw+/vhjADZu3MiXX35JREQEycnJHP79MOt3rcfGYGNeTq1KyMiWlO5Ftu3bRof7OuBu6179dtQBEXwIgiDUIkmSeLOVHyoqCy/fZKiHCy3tq7YVennZMPft28fFixctm7uV5W6rV4q73fNRs0/tBoOBRx99lKioKH799ddyez0Asg3mTeJyjbk1umZNeHqah7IkCbZtn8yI4avRaKq3Vb1k+Vr5n+GRI0dYtmwZHTt2LHE8Ozub+++/nz/96U8888wzbNq/CYcCB0xNTbTxb2MO3CRALryeDIqqsDZmLZ8e+5SFfRZWqw11RQQfgiAItUySJF5r4cf6+FQ+uZbAJ22bVen9RdkwH3jggRLHV6xYQbdu3ejUqVOZ71MluJKTx8JDV9DIErJkTmimkQuTm8kSsiyhlSQi0rUoDmqN9nYpCjwiIyPZs2cPHh4edz0/NsucGjw9P73a17yba4d+JDstEVVVLQ8s3yuoivl7JffPyL5fY2t7hLDwdni4vk3nro9X+7oXY6JIMmjNQUFhHCJJUomgRJIkcrJzmPLoFF77+2t8tfQrEtMS2XF6B7ZaW0L7hBLaJ5SbN24CIGvMv5feHXvzYI8Hy7229ryWd39/l7Gtx9LZu3O121DbRPAhCIJQB2w1MnOb+7Ag8gajvFwYdpccIHfLhlkkIyOD9evX889//rPccvQKXLeX+SI3s+IKurhDX1A15X9qz8rK4vLly5bnUVFRnDhxAnd3d/z9/ZkwYQLHjh3jp59+wmQyWXJzuLu7lzkJMsQ9hHOcw82mesuR7yYnNYGV2yMqebaEY9xIunQxzwFJTnuTsF1v0r3Nxzg2H13pa8o689cj4VeAKxWe/8MPP+Dn40didCJJCUloJS0HN5Scs5GWlmau07lkfH19sXW4e7baR4MfZdPlTbx/5H1Wj1yNrqhSDYwIPgRBEOrItCae7Es15wBZ3r4FQzzKHpK4WzbMIuvWrUNVVSZNmlTu9c4P6US60YRRUVFUlQKTOfmYSVExqiomRcGkqhgVlS+iT7M91wm9tvyb1dGjRxk4cKDledH8k6lTp7Jw4UK2bNkCmHd9LW7Pnj2l8nUAaOXauwUpRnMW2LHdmhDSfwLIEpKsQZLkwq+FzwuzgkqShCE/j9hj/yYqbykmDRy++hJd85JwafknZBvHCq+pd9BwX88N3LB/lDbeD5RY9KKi3p4LosKv234lPTudd/7zDjY2Nmzaugn/Vv74DfLDUeeIt715blDCjQT4BDoP7EyHTh3oE9znrnXQylpe6/EaT+14iq1XtzK29diq//DqgAg+BEEQ6ogsSXzeLpAXzkUz5dRV5rf0KzMHyN2yYRZ59tlnefbZZ+96jp1Wg10lM5xuu2WCXNDeZY7IgAEDKkyJXhVFE06VSmzEVlVFqc71ehtsnSvXs2Jja0fLPvNoyTzCd7fBKCscu7kI36hlhI6seBWJUTGg1+cQFNCC/q0GlHteTEwMk/4xiZ07d1qGzNwc3Gjm1Yzn+j1X4txol2gAegX3onNI50q1o7tvd5q7NOdYwrEGG3yIpbaCIAh1yE4js6J9C2Y082bx1TiOpmfXd5WA2+nVNXW4uUtt7qBbNEeiuoFN/yGR9Gj+PgDx+sRKvcdQuOeOVrr75/qIiAgSExPp1q0bWq0WrVZLeHg4n376KVqtFpOp5qt/hjQbwq5ru8gz5tW4rNoggg9BEIQ6ppEk3mjpR3tHO96MvGFZ5lqfjIVjBFqpBktNq8iSEbSaeT6WLFlCjx49sLPV4+RoT8e2rVkw+yk++b95fPnJBwAoJoWFCxfi7++PnZ0dAwYM4OzZs5Uq37nleDwLPPDIcaj4ZOB83C8A2FSwWmbw4MGcPn2aEydOWB7du3dn8uTJnDhxAo2m5r+Dh1s9TJYhi21R2yo+uR6I4EMQBKEeaCSJxW2acDwzh1U3kuq7OhTd/7V1uDNq0eqP6vZOhIeHM2PGDF6Y/mf+/Ocp2Olk/vPNRlq462jfxJEBzbX8cPgqH374IZ999hlHjhzB19eXoUOHkplZiUm4gEHNwUa++3yP5Ow4Vu4Zgn3q9yTgQ6cmI+56vpOTE+3bty/xcHBwwMPDg/btzTsQp6SkcOLECc6dOwfAxYsXOXHihGUSb0WauzRnWOAwPjv+GTmGnEq9py6J4EMQBKGe9HR15DFfdz67noihHrJ8FqcU9nxoaik7Z5mKknJVMx359u3bmTZtGk28PXi0ZwA//fIbiSnpuHYcyZCnF9J/6gI+X/YVCxYsYNy4cbRv357Vq1eTk5PD2rVrK3WNAikfG235K5P2RH7FL78NwFeJxuDxGBMH7MdGa1et9hS3ZcsWunTpwqhRowCYOHEiXbp04Ysvvqh0GXO7zyUtP40159bUuD7WVqXgo6iLy8nJCW9vb8aOHcvFixdLnCNJUpmPDz74wKoVFwRBuBc839SLuHwDPySm1ms9TKoKqkJQ66Ay/4bPmDHD6te8vRFbzQIvqXAlSXq6OV+Iu7s5u2dUVBTx8fEMGzbMcq5er6d///4cOHCgUmUXyAo6m9LZQtPzkvgqbDSG64uRNA506PwdIzq9g1zNnqOwsDBLdlOAadOmlchPUvRYuHBhpcts4tiEsa3H8v3F7y3zURqKKq12Keri6tGjB0ajkQULFjBs2DDOnTuHg4N5TKy8rZ7Hjx9vvVoLgiDcI9o62jHc05l/RMUzxtsVGysOe+y+dIXojCxUzKnWFbA8QC1cCmreieRcZgGgMOfjOeY5GCqkF6Rz9fJVvv7b12QFZPH3n/5uSb9ePGmW5XvJPJRS4rVi51ieF/Z43EpMxB4HruzeRK6LCxQLdigqRy52DUkmOzuLy1eicHawo2UTL0iOxKg6oKoKc+fOpW/fvpahi6IhCh+fkiuKfHx8uHbtWoU/P8WQg0kLNvqS7993eTU3ry6hicaAwWUE47p8jEbTMPNpTAyZyPeXvueX678wovndh4PqUpWCj+3bt5d4vnLlSry9vYmIiKBfv35A1bZ6FgRBEOD1Fn4MOnKRb+JSeLKJp1XKNCgqT8Sko1iCmQp6FyR/ZGMSX6V/VeJwwt4E9N56Tvuc5vSt05bjZQ2VFB0r8Vo5QysqKu2ybQllBEei8oHKrSgxc4YcOHorDfACYOn3uzl1MZb9+/eXbtodq2pUVa3USpuCDHOiMJ2deZffzLwUfvh9Gr6Gs0gaJ4I6rKCF9/1VqHfda+PWhu4+3fn2/LeNN/i4051dXHe621bPRfLz88nPz7c8z8jIqEmVBEEQGp22jnaM93Hjo+h4HvN1x15T896PApMJRZZ5Wmfkuc5tzenUJZBU8z4mMoAkoRa7CZtMOWikXy2Bgr1sT/O/NOdvc//G/Kfm17hOd7q5fy3+u18gfWo4Tk1DC9OfK6iKyTzMoJjMzy3fq6iqCY2NPaaCXDAZwZjD3L+9x+Fz19i7dy8BAQGW8os+DMfHx+Pn52c5npiYWKo3pCxFwYeNYyAHr35D7JW38ZIN5DqPYHzXhtvbcaeJIROZFz6PiykXCXYPru/qADUIPlRVLdXFdae7bvVcaMmSJbz11lvVrYYgCMI94ZUWvvx4K423Lt/gveCmNS6vaB6Fu05DU8fKLRWFkqm7v//+e9LS0pg2bVqN61MW1Wj+4Cnr7ZHvklm1TA4uqKrKrFmz2LJ1O2FhYbRo0aLEKS1atMDX15ddu3bRpUsXAAoKCggPD+e9996r8BKGrOsAbI/5EjclGpPkSOv2/6G1T9+q1bWO5OemkHb0Pxicm6DYOhcGlhKtVRNtCgrYcvZrXnng7fquJlCD4GPmzJmcOnWqzC6uInfb6rnIG2+8UWKL6IyMDJo2rfl/eIIgCI1JoJ2et1s34dVLsfR2dWSsT832OzEq5kRVNUkatmLFCkaOHIm/v3+N6lIe1WQEQNaU3velMmbMmMHatWvZvHkzTk5OljkeLi4u2NnZIUkSL730Eu+88w5t2rShTZs2vPPOO9jb2/P44xVvHBd3/QhBN7JQ1TPcMOhwtWtC6vW/cgQotmNcyTcV3vApOkcqOvf2+RKFM22KXit2rNg7ixUpmXusMG+MJxXO0yk6JqEiqdAu6iDl9edsBKIyvoXGHHzMmjWLLVu2lOriKq4yWz2DeeaxXl+97YsFQRDuJVP8PQhPSOb5c9d4/my05aZCsZtN0ffmm1DRTUotvBEV3qAAoySDVoemmhNYr127xu7du9m4cWNNm1U+xRx8UM2kWkuXLgUotW/MypUrLb01r776Krm5ubz44oukpqbSs2dPdu7ciZOTU4Xl3zwQQXtdHtftHfFRQcq+ChQPDMw/a0m9/fyO2SXFXrv9uzG/p+QcGKnUnJiSXwGUwrNUyXxcKXxdLTweqbfFXudA/qC/YtI7gmp+FVXF8eZJWhxcCqnXwC2Q+lal4KOoi2vTpk1ldnEVV9FWz4IgCEJJkiTxYUgzcld9g1GjIbR9B5AklMJlluabj3mTOBXJkhJdgcLXC29KqgqY0EoKfwoOrVZdihYUFOWZqA2Wng9t9Xo+KrNEV5IkFi5cWKUlqgDGnCzkZFuM3hLNXr1Rrfo1KEEPwe9fwfkfoc/M+q5N1YKPirq4ilRmq2dBEAShNBd7O94ePpD//ve/DGkfxP196n41haIorFy5kqlTp6LV1uL+o6p5aEjWNLw9Tq9u+6ou063VPr0TtB4M5zY3iOCjSv1xS5cuJT09nQEDBuDn52d53Dm0UpmtngVBEISytWrVij59+vDLL7+Uyp1UF3bv3s3169d56qmnavU6qsmc+Kq6PR+16dy+X83f3EsRSLsxEPs7ZNys75pULfgoK9uaqqqlZkI/++yz5OTk4OJSfkpaQRAEoXyDBg3C29ub//3vf+Tl1e3OpMOGDUNVVYKCgmr1OqpS1PPRsJas5iRcIyquADtbnXmCxb0iaATIOvPQSz0Te7sIgiA0QFqtlgkTJpCVlcXGjRtRlOptvtagFfZ8SA1s2OXi5mWoSPi0qt3gq87ZuULL/iL4EARBEMrn6enJ+PHjuXTpEmFhYfVdHatTFRMqDW/Ox/mjx2nho8HG0bVWr7NkyRLLcuAiCQkJTJs2DX9/f+zt7RkxYgSRkZHWu2jQCLh+EPLSrVdmNYjgQxAEoQELCgpi0KBB7N2717K9+j1DMaI0sNtQ1N4fiUuXadmtJyogSRWvqKmOI0eOsGzZMjp27Gg5pqoqY8eO5erVq2zevJnjx48TGBjIkCFDyM7Ots6Fg4ablzhf+dU65VVTwwo3BUEQhFIeeOABblyLZuviv7LNWAD624kb1WI5Ju48KMkaTDodJhs7Rk97mmZtQ5EAWZaRZRlJltDImsLv5dubv9UgMVmVKCYUZO7M8mHIzWLPu7PIz86i3cARtBoxFWRN6YRetWD/d18DEDzmWRJ/WFQr802zsrKYPHkyy5cvZ9GiRZbjkZGRHDp0iDNnzhAaal4i/fnnn+Pt7c23337L008/XfOLuzYDjzYQvR9CH6l5edUkgg9BEIQGTpIkxo4fz+c/fQ+AZ6tgNHo95p1fKbYLbOH5hYnF8nJziY++ijYjhR0fLq7ydUt/5r8zm2fhl8JgR5Wq1oshqQoSvdBOGmlJmCZJKnmm27emS2s2Yf/NHckqSwQh1gkPVMwxW4FJpn1LZ2xdvVAluVTyL2uYMWMGo0aNYsiQISWCj6J9zopnBddoNNjY2LB//37rBB9g/vlVM6ustYjgQxAEoRGws3fg2c9XsfLl5wls2ZIBU6ZX+r35eXkc3v4TBbm55mRkqjnrpaKYN20rPplVVQu/Vy3/V7jhW9Hhom9US69Lxo3r5KWn4dW2Q+mLl5EIrKiMmEN7Mebl0advaGF5KqpiTpWm09vj23Ug14/uNddDNW8sh6oU+1rsYSkbSgQkpWKTsoMVqTDNuSxLhIx5+vZRqbANVup1WbduHceOHePIkSOlXgsJCSEwMJA33niDL7/8EgcHBz788EPi4+Otu+Q6NxXsyt4Qtq6I4EMQBKGRcPLwpOPQkZzft6dKwYfe1pZ+YyeU+drSpUtZunQp0dHRAISGhvK3v/2NkSNHljr3ueeeY9myZXz00UclJklW1w9ZGVw5eojuM/5R7jl+9w27axlLlixh48aNXLhwATs7O/r06cN7771HcHDZu7dWrQ2FPTmqAlL1UsAXFxMTw5w5c9i5c2eZe57pdDo2bNjA9OnTcXd3R6PRMGTIkDJ/F9Wmqubgw75mewfVVMOa6SMIgiDclbOnFwU5OVYrLyAggHfffZejR49y9OhRBg0axJgxYzh79myJ83744QcOHz5s1U3mrNGZEB4ezowZMzh06BC7du3CaDQybNiwMidoVrUNalEFVessc46IiCAxMZFu3bqh1WrRarWEh4fz6aefotVqMZlMdOvWjRMnTpCWlkZcXBzbt28nOTn5rtuZVImqAlKZPVJ1SfR8CIIgNCImgwGNznpJuUaPHl3i+eLFi1m6dCmHDh2yTHq8ceMGM2fOZMeOHVbe66Xm0cf27dtLPC/akyYiIoJ+/fpZjlevDcV6Pqxg8ODBnD59usSxJ598kpCQEF577TU0xTbYK0rSGRkZydGjR3n7bSvtRivL4OAFWQnWKa+aRPAhCILQiCgmk2VCqbWZTCbWr19PdnY2vXv3Nl9PUZgyZQqvvPKKJRixmlpYSpKebs5f4e5+e05DtdtQ9HMuzMRaU05OTrRv377EMQcHBzw8PCzH169fj5eXF82aNeP06dPMmTOHsWPHMmzY3YefKs1khOxb4OhjnfKqSQQfgiAIjYiLtw95WZnkpKdh7+JqlTJPnz5N7969ycvLw9HRkU2bNtGuXTsA3nvvPbRaLbNnz7bKtYqzdhClqipz586lb9++JW7y1W6DXNgTYaWej8qIi4tj7ty5JCQk4OfnxxNPPMGbb75pvQukXwfFAB6trVdmNYjgQxAEoRFp1qEzskbD+f1hdBs11iplBgcHW+YZbNiwgalTpxIeHk5ubi6ffPIJx44dq5XcH3LhzV1VFKsEIjNnzuTUqVPs37/fciwiIqL6bZCsO+xSljsz186ePbtWAj2L/EzzVzvX2rtGJYgJp4IgCI2IvbMLbR8YyOFN35OXnWWVMm1sbGjdujXdu3dnyZIldOrUiU8++YR9+/aRmJhIs2bNLBMkr127xl/+8heaN29e4+vKhXMcVCtMfpw1axZbtmxhz549BAQEWI7XpA2SJfiwzrBLg6AYzV+tsHqnJkTPhyAIQiPT97EpRB7+jd3L/82oOa9avVdCVVXy8/OZMmUKQ4YMKfHa8OHDmTJlCk8++WSNryMX9naYc4tU72aoqiqzZs1i06ZNhIWFlVoVUqM2FO+ZqVbtGqCinC6yCD4EQRCEKnB092DI9Bf5+bN/0vXBMfgHhVS7rPnz5zNy5EiaNm1KZmYm69atIywsjO3bt+Ph4YGHh0eJ83U6Hb6+vuXm0aiKop4Pk9GIRlu9FTwzZsxg7dq1bN68GScnJ+Lj4wHzahE7O7uatUGy7oTTBsGYa/6qLZ1npC6JYRdBEIRGKPj+frj4+HJ8e822R09ISGDKlCkEBwczePBgDh8+zPbt2xk6dKiValo+qTD4UIzVv7kvXbqU9PR0BgwYgJ+fn+Xx3XffVfzmihT2DiimgpqX1VCkRJmDKmfr5WupDtHzIQiC0AjJsoauI0YT9t8V9Hl0Mm6+1buZrFixokrnF2VCtYaiCacmo6HM1xVFwaQomEwKRqMJk8mE0WTCZDQfN5pMRF27iV5vg5+PR5lllKWybZA05t4YxWCs5qBQAxR/yryxnM6uXqshgg9BEIRGqsPg4UT8vJkdSz/hsYXv1t1utFaSlm+ef/Dp80+BZN6gTkK1fJWrsKlb31ffpWe39hWfWAWWYSFTAdZL61bP4k6BX6f6roUIPgRBEBornd6WQU8+zw/v/x+J0VfxadGqvqtUJQY3c2+NTeeB6O3skTQyslz40MhIkowsa5BLHNeU+D4jNY3UHf8lJSnF6vUr6vlQy+mZaXRMBog/De3G1HdNRPAhCILQmDXv1BW9gwOXjxxsdMGH3s7c9T/hz48RGFC9jJsXr8Tw047/YjJZf1KopDXfIhXDPRJ8JF0yTzht0rW+ayImnAqCIDRmGq2WVl3v4+KBvVbJl1GXNBrzzb2gBjd3jbZoUqj1gw9ZY2Mu25Bv9bLrxY0I82RT3w71XRPR8yEIgtDYdRg0nHP79nDt9Amad+xS39Up0+WrMURfi8VoNKEUThw9f+Y8WqCgwFjtcrWFAUxtBB+SjXk5qmLItXrZ9SL+tHmyqd6pvmsigg9BEITGrknbUHxatmHfN6to2q59tXNm1Kb1b87FxljyJq4FTMg4OzlUu1xtUc+HUv0ApjyS1jwspN4rwUfSJfAKqu9aACL4EARBaPQkSWLI0y/y7ZuvsGfVMgZPf7HBrXzRmvLJb30fD0+ejFanQaPRoNNqcXZ2xN21+p/Ebycqs/7+K5LO3POh5t8jwUd6LASNqO9aACL4EARBuCf4tmrD4KeeZ9fyz1BMJgZPfxGNtuH8iTfKOvSuXrRvZ91JsVpd4bBLLWQhlfT2AKiGPKuXXedU1Rx8uARUfG4daDj/MgVBEIQa6ThkBBqdjp1ffkpybAwDpz6Db+uG0c2uSJpaSVOuLdqcrjYmnGr15rKN9d/zEfuvP6G/dZwczy5og4fh3nMsdq5elS8gOwmMeeDStPYqWQUi+BAEQbiHhPYfjIu3D7+sWMo3C+aS6dUG2cEZrVx6GKbMkZmKFsyU8R4nTx9mvPQcSFK5wz2qJKEq1h8aKVrtUhtLbckx5w7xCJtL7p4FAJjTnwGWr6CqUrHn5q9q4Q/KcuyOn+t74Wm8vy+jxDFvB5nzLzcpdS5AgDYdZMhNCsMuZSeG317lpuKN4tIUra0jaLQg60DWgkaHlJOEFDwSe7/WqBodcnYCDgD27jX9qViFCD4EQRDuMQFt2zPlvU/Zun4jYT/vQMow3+T0WhmNVPymWHOuWTfJvwIfTtpqOXb75iuhSubv7VQTSNZfCmyr06AgYTBaf8Jplo0PJ+Jb4uhoi7ufH5aQQlULAzfV8r2EWvhDvR12SIXtlVTVErRJSCCBwS6SIF+Fb5+7j6IXtRrIddSbn1vON///TVRsez2J231jyTi/j5zjP6CNOYCUfgUp1YiEgoyCLCnoJBP2WgMcCC/dplwTjlb/SVWdCD4EQRDuQbJGw+iJf6LzsFG8u+0C287E4aDXMiLUlxHtfbmvhTtOtmWviknOyudWVj4hvs4VXictI5s1637EZDCaU6MDEkpR6GG5WcuyxPAHrb9ZnY1GRpFkDAbr93zo7Bw5ntqE+4f+mdbjJ1q1bOeMhdgl/EDXhb9U/b2hA3AOHXDXc3Jiz5MecwnVaEQyFXDzwinOHzrIo4HdqldhKxPBhyAIwj2sqbs9/57cldjUHNb9HsPW03Gsj4hFlqCFpwP+rna42JmDkPRcAzdSc7malI0kwdZZD9DO/+4BiKuzA7Ofrf6Nee/evXzwwQdEREQQFxfHpk2bGDt2rOV1VVV56623WLZsGampqfTs2ZN///vfhIaGAuaVPioSxlro+dDpzXM+DIba2dU2MjISf39/9Ho9PXv25J133qFly5ZWKds+oC32AW0tz89eTsXokY6Nnb1Vyq8pEXwIgiD8AQS42TNveDB/GRbE1aRsDl9NITIxk/j0PFKyzTdXJ1st/YO9mDW4Nf/YcYlvDl9j8SO1mw0zOzubTp068eSTTzJ+/PhSr7///vt8+OGHrFq1iqCgIBYtWsTQoUO5ePEiTk7mJbqqJJNwJIyFM0+iKiqoJlAUUJXCryqoCpKq3PFVLfxeRVaVwqETBVlVkVDQqubelJg06wcfPXv2ZM2aNQQFBZGQkMCiRYvo06cPZ8+excOj8jv0VlbKjRg8mgZavdzqEsGHIAjCH4gkSbTycqSV191H/k/GpLPzbDyqqtZqzpCRI0cycuTIMl9TVZWPP/6YBQsWMG7cOABWr16Nj48Pa9eu5bnnngNA274v+XGxIMvIWhlJNj+QNUjS7eeSbN6Urui5rNGArEEjy0gaDZIsoyn8Kms0mBSVH88l87Bzk1ppd5EOHTrQu3dvWrVqxerVq5k7d65Vr6WqKqlxN/ALCrFquTUhgg9BEAShlP5BXqw6EM215Byae1Y/A2lNREVFER8fz7BhwyzH9Ho9/fv358CBA5bg45UF1r1ZFykwGPngzR2MVGo/YZuDgwMdOnQgMjLS6mUnx14nKzWFgJBQq5ddXWJjOUEQBKGUtn7muR5XbmXVWx3i4+MB8PEpueOtj4+P5bXapCtcxltQC5NZ75Sfn8/58+fx8/OzetnXz5xCo9USEFr/G8oVEcGHIAiCUIqnow2SBLcy639H1zuHfWp7KKj4dWXVRIHR+sHHvHnzCA8PJyoqisOHDzNhwgQyMjKYOnWq1a+VlZqMo7sHOhu91cuuLjHsIgiCIJSi1ci429uQWI/Bh6+vL2DuASneI5CYmFiqN6S2aFDIN1o/P0lsbCyTJk0iKSkJLy8vevXqxaFDhwgMtP6k0PSEeJy96ubnVVki+BAEQRDK1MbHkdM30uvt+i1atMDX15ddu3bRpUsXAAoKCggPD+e9996rkzpoVAVDLWxat27dOquXWZ7Um7H4B7et+MQ6JIIPQRAEoUw9mrvz7e8xtTrMkZWVxeXLly3Po6KiOHHiBO7u7jRr1oyXXnqJd955hzZt2tCmTRveeecd7O3tefzxx2ulPnfSoJJXC8FHXVEVhdS4m4QOsH6Ct5oQwYcgCIJQplB/Z5IKs516O9nWyjWOHj3KwIEDLc+LlplOnTqVVatW8eqrr5Kbm8uLL75oSTK2c+dOS46P2mYjmWpl2KWuZCTdwmgowL1Jw9jNtogIPgRBEIQyNXE1Z8NMzKi94GPAgAGoZe2kVkiSJBYuXMjChQtr5foV0aGQX/uLXWpNatwNAFx9fOu5JiVVabXLkiVL6NGjB05OTnh7ezN27FguXrxY6rzz58/z8MMP4+LigpOTE7169eL69etWq7QgCIJQ+/xczQFHfS63rW9aScVoarzDLjcunsPOyRlXH+sv4a2JKgUf4eHhzJgxg0OHDrFr1y6MRiPDhg0jOzvbcs6VK1fo27cvISEhhIWFcfLkSd58801sbWsnahYEQRBqh6ejnnZ+zmw+cbO+q1JvtBIYTI132CUz6Raufv7mjK8NSJWGXbZv317i+cqVK/H29iYiIoJ+/foBsGDBAh588EHef/99y3nW2ihHEARBqFvP9W/JnHUn+D0qhftauNd3deqcVgKD0niDj6zUFBxdG97vrUahUHq6eQmWu7u5YYqisHXrVoKCghg+fDje3t707NmTH374odwy8vPzycjIKPEQBEEQGobRHf1p38SZd34+f9e5GfcqjQyNeLELWSnJOLg1vOCj2hNOVVVl7ty59O3bl/bt2wPmxC9ZWVm8++67LFq0iPfee4/t27czbtw49uzZQ//+/UuVs2TJEt56663qt0AQBEGoNbIsMf/Btjy+/DA/n45nVMeGNXegtvWMOcV9l45wJOxLVEkCCZBkVAmQJUBCLRrSkGVwdaXH19+g0eqsWo/c2FgSf9mNKS8XY24eSn4eprw8clKSSYi8iG9gC2QVVIMB1WgAgxHVaMT/ZizuXXpYtS7WIKnVDGVnzJjB1q1b2b9/PwEB5iU8N2/epEmTJkyaNIm1a9dazn344YdxcHDg22+/LVVOfn4++fm3M+hlZGTQtGlT0tPTcXZ2rk7VBEEQBCub+tXvXE/JYffc/mjk2k9t3lBsGPU4ATcikZv6gKqAqiIpKqjFHwqSoiJlZWGXnklWUCuwsSmjtNK324ruwOb0KhKOZ86Xek2RQJEkFEkyB0YaGUWWUWUNaGRUWcYhLQOnv7xMwDPPVqv9VZGRkYGLi0ul7t/V6vmYNWsWW7ZsYe/evZbAA8DT0xOtVku7du1KnN+2bVv2799fZll6vR69vuHkmxcEQRBKmzGwNY9+eZAzN9Lp1NS1vqtTZ3Q2OpJ8mzPqx/UVnpt84jhXZ89CSkqudPmVDeOy3VwwBQfR5u9voXOwR+fggE5vi0Zb/m1cyc/nYqfOOHk3rNTqUMXgQ1VVZs2axaZNmwgLC6NFixYlXrexsaFHjx6llt9eunSpVvLVC4IgCHUj2Nec1CsyMesPFXxUhUfnLnjsLfuDdn1QMjMBkOsoIVtVVCn4mDFjBmvXrmXz5s04OTlZtjR2cXHBzs4OgFdeeYXHHnuMfv36MXDgQLZv386PP/5IWFiY1SsvCIIg1A0XOx2dmrry06mbTOjWsLJlChVogBOFq7TaZenSpaSnpzNgwAD8/Pwsj++++85yziOPPMIXX3zB+++/T4cOHfjPf/7Dhg0b6Nu3r9UrLwiCINSdp+5vTtjFWxy6Wvlhhcau4d22q0CjAcz7uzQ0VR52qYynnnqKp556qloVEgRBEBqm0R39WflbNAu3nOWnWX3RahpW4iqhbPkXLsLQhrWxnPiXIwiCIFSKLEu89XAoFxMyWX3wWn1Xp26oaqUnhTY0msIVJ9kHDtRzTUoTwYcgCIJQaZ2aujK1d3M+2HGBo9Ep9V2dWqeoIDXSpcWmFPPvx+Pp6fVck9LErraCIAhClbw+MoTzN9P58uOj7JZ05NlI5t6BO0fm1TKWkqp3nFj8HLXky1Kxg3eWYzAq5sAA0Gtly+vFz5OKXabsetw+Ty1+XrFKOHhPxD377J3vbhQKCjd0tWnWrJ5rUpoIPgRBEIQqsdVpWPZEd/77l/1IqorWRoOiKby9lxUFQFG2rNKvFb2tMHuoZDkmlV2WZA50ZJNCVFI2/q626G11Jc4rCiTu/GqphiRhvpxU+Nx8XCos21IXCTifCfY1345+yZIlzJ8/nzlz5vDxxx9jMBj461//ys8//8zVq1dxcXFhyJAhvPvuu/j7+9f4egB5Fy+CVotOBB+CIAjCvcDF3obH/9aT9UuOcF8XX/pNDKrvKtWKjS9+jbGGwy5Hjhxh2bJldOzY0XIsJyeHY8eO8eabb9KpUydSU1N56aWXePjhhzl69GhNqw2AkpWNpNMh6ayb6t0axJwPQRAEoVrc/Rzo+XBLTofHEn06qb6r0yBlZWUxefJkli9fjpubm+W4i4sLu3bt4tFHHyU4OJhevXrxr3/9i4iICK4XDpfUlJqXa/5GqlnwVBtE8CEIgiBUW8dBTWnewZMdy85w5VhifVfH+mqY6GPGjBmMGjWKIUOGVHhueno6kiTh6upas4tiXuGStPw/uD36J/MwUgMjhl0EQRCEapNlieFPh/LL6vNsX3aGNj186PlwC1y87Ou7atZTzXv3unXrOHbsGEeOHKnw3Ly8PF5//XUef/xxq2yqmvTlMmzbtsV73rwal1UbRPAhCIIg1IjWRsOwp0NpFurOwR+uEvlmAk2C3Wja1g2vZk44uOjRaM0d7SajQmZKHvFX0kmJy6ZlZy9ad/NGa6Op51ZYV0xMDHPmzGHnzp3Y2tre9VyDwcDEiRNRFIXPP/+8xtdWVZX8S5dwe/zxBjnfA0TwIQiCIFiBJEm07eNP6+4+XD6ayOWIBI5uu4Yx31Tm+baOOly97fhl9Xn2fR9Jmx4+dB3WDGdPuzquee2IiIggMTGRbt26WY6ZTCb27t3LZ599Rn5+PhqNBoPBwKOPPkpUVBS//vqrVXo9lMxMTKmp6Fu1rHFZtUUEH4IgCILV6Gw0tO3jR9s+figmhay0fLJT81FMKpIMkizj5K7HwUWPJEuk38rh/G9xHN99ndS4bB75S9f6boJVDB48mNOnT5c49uSTTxISEsJrr71WIvCIjIxkz549eHh4WOXaucePA2DTurVVyqsNIvgQBEEQaoWskXH2sMPZo/zeDBcve3qNbYWrjz2/rD5Panw2br4OdVjL2uHk5ET79u1LHHNwcMDDw4P27dtjNBqZMGECx44d46effsJkMll2ind3d8fGxqba186/fBnZyQnboIa7/FmsdhEEQRDqXevu3jh52LLn6wuYDA1vF1Zri42NZcuWLcTGxtK5c+cSO8UfqOFeLMbkFDTubhWfWI9Ez4cgCIJQ77Q6DUOfCmXzR8f5eekphk4PxdahYU6WrK6wsDDL982bN6/0TvFVJdvbo2Rk1krZ1iJ6PgRBEIQGwa+VC6NmdCQhOoO1Cw9x8pcY8rIN9V2tRke2s0M1Guu7Gnclgg9BEAShwWja1p2Jb/YkMNSDAxsus/r134i/ml6vdWp4KbruzpicjGzfsPOsiOBDEARBaFAc3fQMntaOqe/ej7u/AzuWnyE3s6C+q9UoqKpK5s6d2HXuXN9VuSsRfAiCIAgNkr2zDSOe64DJqLDl0xMiAKkMRQFZJnv/foypqfVdm3KJ4EMQBEFosJzcbRnzUheyUvLZ/7/I+q5OgydpNAR+/V9Uk4n0DRvquzrlEsGHIAiC0KB5NHGky7BmXIm4JSagVoLW0xPZyRFTekZ9V6VcIvgQBEEQGryQ3n6oqsqFg3H1XZUGzZiaSvxb/4fpVhKOgwbWd3XKJYIPQRAEocGzd7YhqKcvEduukZMh5n6UJWPbNi4PGEj6pk34vvUW9l261HeVyiWCD0EQBKFR6DWmJbJGYuu/T5KXJYZfilMKCkh4733su3enddge3B57tL6rdFci+BAEQRAaBQcXPaNmdCQjKY//vX8UQ0HZO+b+EaWtX48xMRGfBfPRurvXd3UqJIIPQRAEodHwDnRm1MyOpCfmcuNC3SwlTZT8yUlsuMtWM3bsJPHd93AZMwZ9y5b1XZ1KEXu7CIIgCI2KT3NnXH3siTp5i+YdPWv1Wi5uGuJS4NKmw3R+bkStXutuEg+f4/zmCK7HadBrTbS935fc5Gwyk3PJuhRN+/sH4vfWwnqrX1WJ4EMQBEFoXFQoyDWis6v9W9gD88dyYd5BVKXud9pNu3ids+sPEXXVSLqNL7LJCy/7NG4ZPNh7SIOkOqBKzuDpQ4/pgUg2NnVex+oSwYcgCILQqMRdTScno4CWnWq316OEWtqBtizx4cfYvuoy2TbuSKorPnZJdOwBIeP7Y+NkR2ZMIqa8AnB25YcPT+DkYYdf98Yx3FJEBB+CIAhCo3Lp9wQc3fT4tXKt9WtJUt1vK3d8/XGy9S3o3iKFDk8Nxd7LpcTrTk29yckoYMP7R9HoZEY8175e6lkTIvgQBEEQGg1FUbkckUBoX38kue5uuHXV8XFm1S6uGgJp5ZxAz9cmlVMXlV9Wn8eQb2LCa91xcNHXTeWsSAQfgiAIQqORFJNJfraR5h3qZsilqEMhLzOv1q6Rev4a0b+eRlXh4Bl79MYMhi0pP0/Huf03uX42mVEzOuLsaVdr9apNIvgQBEEQGo3stHwAnL3q5qYr6cy3ycSYXKuVmR0di6LIHFu2iwupPhglG8AeAGdDIh3uc0XWasp8b8y5FPauu0ToA/51FoDVBhF8CIIgCA3WkiVLmD9/PnPmzOHjjz9GZ6tl69HVfNzteW7G3cDGxoZu3bqxePFievbsafXra3RanAy30NlW/b2mnBxSjl8kMyGTbTuNZZzRFCQI8cugINeAixN0mjoQhwCfMsvLyzawe/U5AoLdeGBiUNUr1ICI4EMQBEFokI4cOcKyZcvo2LGj5Zh3oBO+7k0ZPX4Ywx/rTW5uLh999BHDhg3j8uXLeHl5Wb0eMrcnfFz4zxac27bC//7Qcs8//u+fuHgglmSnigOEYUN0tJkwtsLzVEXll1XnMBkUBvw5BI2mcecIFcGHIAiC0OBkZWUxefJkli9fzqJFiyzHbWy1TJo4ifir6TRv3gJZlvjwww9ZsWIFp06dYvDgwdavjATRef58O201KbZN4WgCXcLOETK2BzePRZESnUyPF4ZyfPlujl9zA+zhjsDDW7mBh7tE6IQeOPp5kJ+VR96tNPz7tq9UFc4fjCP6dDKjXuyIk3s1umEaGBF8CIIgCA3OjBkzGDVqFEOGDCkRfAB0HNSUS78ncP63m7Tp6cWyZctwcXGhU6dOtVKX3qMDiToUTWSyr+XY8RgPjv/rauEzd07/NQJwK/G+QG0MLp423P/mo8iaknM4HACCAyp1fVVVObz5Kj4tnGs9o2tdEcGHIAiC0KCsW7eOY8eOceTIkTJf92nuTJrdBboNfogCYx5+fn7s2rULT8/auTG3GtObVmN60/VkJKc3HedMfNnX0Sm5GGTzRFgHUxoPfTHVKtc/tSeWnIwC+k1q3PM8ihPBhyAIgtBgxMTEMGfOHHbu3ImtbfnDC0+9/CiGBCeC+rnwy5HNPProoxw+fBhvb+9aq5t7pzY80L4V3RNTubrtGL7dW6Gz1aIYFAqy83DwduXW2RgURaXlyH5WuWZ8VDr7v4/Eyd2WVl1qr211TVLVOswZWwkZGRm4uLiQnp6Os7NzfVdHEARBqEM//PADjzzyCJpiwxQmkwlJkpBlmfz8fMtr2744TUpcNpP+dh/BIcE89dRTvPHGG/VVdatLS8hhw/sRuHjbMfblLmhtyl5+21BU5f4tej4EQRCEBmPw4MGcPn26xLEnn3ySkJAQXnvttRJBSdcRgWx47ygR26+hqir5+fl1Xd1aoSoqV47f4rf/RWLnpOOhmZ0afOBRVVVaq7NkyRJ69OiBk5MT3t7ejB07losXL5Y4Z9q0aUiSVOLRq1cvq1ZaEARBuDc5OTnRvn37Eg8HBwc8PDxo37492dnZzJ8/n0OHDpEnpeIYnM2ceTOIuR7LhAkT6rv6NZaRlMt//3qQHcvP4ObnwOjZnbF10NV3tayuSj0f4eHhzJgxgx49emA0GlmwYAHDhg3j3LlzODg4WM4bMWIEK1eutDy3aUTb/AqCIAgNl0aj4cKFC6xevZqkpCQ8PDxoFdCOOQ99SNQeE35uWXg0cazvalZb3JV0MlPyGPl8B1p2tn7OkoaiRnM+bt26hbe3N+Hh4fTrZ55cM23aNNLS0vjhhx+qVaaY8yEIgiBUVdTJW+xfH0lmch49HmpB95HN63TjOWu4GZnKzv+cxdXHnrFzu9Z3daqszuZ8pKenA+Du7l7ieFhYGN7e3ri6utK/f38WL15c7gzk/Pz8EuN0GRkZNamSIAiC8AeSm1lAVmo+egcdI57twMXD8fz+UxSp8TkMntoWjbbhZwItyDOy8YNjJN/IwjvQiaHTy8+eeq+ods+HqqqMGTOG1NRU9u3bZzn+3Xff4ejoSGBgIFFRUbz55psYjUYiIiLQ60tv+7tw4ULeeuutUsdFz4cgCMIfT0ZyLreuZ5KemEt6Ui4Zt3LJyzYA4OCix9nDFo2NhpSb2STfyLJsNGchQVE2dI8mDjy24L4G3wNy5Xgi2788Q98/taHjwIAGX9/yVKXno9rBx4wZM9i6dSv79+8nIKD8LG1xcXEEBgaybt06xo0bV+r1sno+mjZtKoIPQRCEP4j8HAMXDsZzdt8NUuNzANDZanDxssPFyx47Jx2okJWWT0ZSLsYCE25+Dnj4O+LVzAlnT1u0NhoKco2kxudw81IqFw7FA/D4wp64+Trc7fL1QlVV0hNzuXI8kaM/R+PX2pXRszohSY0z8IA6GHaZNWsWW7ZsYe/evXcNPAD8/PwIDAwkMjKyzNf1en2ZPSKCIAjCvS35ZhbHd17nckQiqkmlZVcveo1phW8rF+ycdNW6Efu2dKFtHz8emBhE/JV0XH3sa6HmNXNm7w2Obb9GZkoeskai3f3+9BnfulEHHlVVpeBDVVVmzZrFpk2bCAsLo0WLFhW+Jzk5mZiYGPz8/KpdSUEQBOHekXIzm8NbrnL1xC0c3fTc91ALgnv54uBivQ+iNrZamoV6WK08a8jLNnDylxiOboumTTdv+k0Kwr+NKza2f7yUW1Vq8YwZM1i7di2bN2/GycmJ+Hhzt5aLiwt2dnZkZWWxcOFCxo8fj5+fH9HR0cyfPx9PT08eeeSRWmmAIAiC0PAZDSaiTiZx7Uwykb8n4OiuZ+CUEIJ7+jaKSaE1kZNRwG8bIrkScQtVVek+sjn3PdSi0c7tsIYqzfkor0to5cqVTJs2jdzcXMaOHcvx48dJS0vDz8+PgQMH8vbbb9O0adNKXUMstRUEQbh3mIwKZ/fd5MhPUeRlG/Bo4kCbHj50HtwMje7eDjoAYi6ksGP5GQC6Dgu0eg9PQ1Jrcz4qilPs7OzYsWNHVYoUBEEQ7kGZKXlcPBTP+QM3yUjOo21vP7oOD2yQczBqg6qonPglhgMbLtO0rRtDp4di5ygSbhb54w00CYIgCLUm+WYWR36K5urxRDQ6mVZdvHnwhWaNOutoVaiKStzVdA5uvEL81XQ6D2lK73Gtkf/AQyxlEcGHIAiCUCN52QYSojK4fi6ZM2E3cHTX88BjQQT38v3DTKYsyDVyak8sZ8JjyU4vwM3XnrFzu9AkyK2+q9Yg/TH+VQiCINSCgrxcslJSSLgaya3r0WQm3cJYYM5blJ+djdFoIO7SBQCGv/ASLTp3w8H13rgZqapK/NUMTv0aw5Xjt1AVFVtHHV1HBtJ9RPM/xHwOgOy0fE7tieXsvhsYCky07eNP0H0++LZ0Eb0ddyGCD0EQhEoqyMvl0sH9XIn4nbjIC2SnpVpec/byxtnLG53eFlQVBzd3NDqdJfjY8cUnSJJEmx696fPoZDwCmtVXM2qkINdI7IVUju28RkJUBi5edtw/oTXNO3jg7Gn3h8lVoZgUTuyO4fCWq2h0Mu36+tN5cDMc3e7NyaTWVqON5WqDWO0iCEJDYywo4MiWDRz9aRMFebn4B7WlabsOeAQ0xcHVDc9mzbF3drlrGTkZ6Vw8uI+IrT+QmXSL+x+bQo+Hx9fLzdqQb8JoMKEqoHfQotGU3UuRl2XgZmQaCdHpJMVmkXIzm6xUc8+OfxtXugxtRmB7j3t+yaiqqKQl5nAzMo2rx2+hKCrJN7LIzTLQeXBTuo9qgd5OfJavk/TqtUUEH4IgNCRXIn5nz6ovyUxOpsuIUXR9cAzOnmVvlFkZRoOBg+u/4ffN/6PLiNEMnPZsrQcgqfHZRB5NJOZcCqnx2eTnGEu8buuowzPAEd+WLgS298BkVDi+6zrXz6agKioOLjZ4NXPC3d8Bdz8HPJs6/WEmkGam5PHrmvPEXkgFCfxbu2LnqMPVx55WXb3xauZU31VsMOpsV1tBEIR7laqq7Pt2NUc2/4/mnbvxyOsL8WhSuXxFd6PV6Xjg8Wk4e/mw+z//xrNZczoOHm6FGpeWnZ7P/vWRXD6aiI2dlqZt3Wje0QNHN1t0eg2SBLlZBrLT8km8lsmZvTc4+nM0AB4BjvR7rA3NO3ri6GZbK/VrqApyjVwvDNROh8Uia2QGPRFC846eYrmslYjgQxAEoQy/ffc1Rzb/j/5/fopuDz1i9d6JTkNHEhd5gX1rV9H2/v7obK17g0+IyuCnf59EkmDgnwsziVYwCVRRVOIup6GYVAJC3P4w8zeK5GQUEHkkgeO7rpOdlo/OVkPLzl70fqTVPZsYrL6I4EMQBOEOsefOcHjTd/SdNJXuo0vvxm0tvSc8zrm9e7hwYC8dBg2zWrkJURls/vg4Hk0cefDFDpX+tC7L0h9uaaiqqqQl5HDy11guHIhDVVRadfPmvoda4OL9x5lAW9dE8CEIgnCHA/9bi0/LNtz38PhavY6Ltw/eLVoRe/6M1YKP9Fu5/PjZCTyaODJ6dqc/TJ6N6jj3mznte1ZqPraOOrqPak77fk2wddDVd9XueeJfpSAIQjE5GenEnD3F8OfnIMm1n6vCycOTnIx0q5SlKCq7V55Db6floZkd/7CBR1ZqHrlZBhKupnNidwx2Tja06uqFi5cdkiyRl23g+tkUIo8k0KqLF/0f96NJsBs6G019V/0P44/5L1MQBKEcKbExAPi1CamT6+VmpuPo7mmVsi4fTSD+ajqP/KUrevs/1qf3gjwj5/bfJPp0MjcumvOvSLJEkyBXVBUObb6KyaBYznfysGXglBDa9vETQyv1QAQfgiAIxZiM5mWoGl3t37zzsrKIvxJJv573W6W8k7/E0CzUA/82rlYpryEzGRWSYrJIT8ohMzmPCwfjSUvMoUmQK0OmtcXNzwEnD1vLfBdFUcnLMqCYFGwddGhFL0e9EsGHIAhCMc5eXgCk3IzB1ce3xGu/ff8NMWdP0bxjF0IHDsHW0YmEy5E4uLnh5tcEAJPRwIH1a7l58XyF10qNuwFAcJ9+Na530XJZZ09bNv3zWInXcjIK6P5gc4J7+pbz7obPaDCRlpDD1RNJ3LyUSnxUhqUnQ2erISDYjRHPti83/4gsS9g7i2WyDYUIPgRBEIpx9fXHxceX8/vCaNmlR4nXLh4Ix1hg4OCGdfz2/dclXvNrE4y9ixvxly+Sk5FOq249K1w+6+TpRevuPa2y34uNnZb2/ZtQkFcygZhiUrkZmUb6rdwaX6M2XTgYR+TRBLwDnfFr5YKskYg6lUTUySRyMwswFpgDDRs7LU2CXOk1piW+rVxw93MozFkihk4aExF8CIIgFCNJEt0fGscvXy2l05CRBLRrX+L1kPv70f2hR7hx6TwFOTm4+TUhNe4G0SePkZedRUjfAXQYOLTO927R6TX0nxRc6rixwMTlo4k4uTfcPBUZSbn8strcU5QQnWFJdGbnpKNNdx+cPGyxddTh5G6LTwtntDoxZNLYieBDEAThDh0HD+fiwb1s+fAdJvx1Ed7NWwLmnBAA9i6utOnR23K+f1AIof0H10tdK6K10eDZ1JHzB+II6ulb5j4uBXlGft8SRWB7D5q2c6/zOjp52OLm54BPcycGTmlLVkoeRoOCq4+92Bn2HvXH2PNYEAShCmSNhof/sgBnL2++/dsrnNixFcVkqu9qVVvfCW1IiMrg+8VHOLotlKmaYgAADZtJREFUmgsH47h4ON6c52JrFN/+32FO/hrDT5+fJCejoFrXiL2QwtFt0aQl5FT5vcYCBWO+CZNRRZYlnD3tcPdzEIHHPUxsLCcIglAOQ14ee9Ys5/QvO3Dx8SU9IZ7uo8fR/89P1XfVqizxmnk4I+ZCKsb824GU3l5L03budBrclJ/+dRJbRx1dhwfi7GGLs6cdju62lQoC/v38rwC4eNnx57d7V3C2maHAREJUBoc3XyHxWiYTXusuNmprxMSutoIgCFaUEHWFiJ82cX5/GAOnPUfXkaPru0rVpioqiqKiqiqSLJUYhklLyGHP1xe4GZlmOda0nTsPz+5cYbnnD9zk1zUXAHhx6cByJ4CqikpSbBYHN10m5rw5H4eLlx1DnmyHb0uX6jdMqHci+BAEQagFqqLUSdbT+mbIN5Gdls83fz9Eu77+DPxzxQnXIo8msPM/ZwHoNiKQLsOaobfXkZNRQOK1DBKiMkiISichOpOCXCNOHrZ0GtQU/yBXPJs4IokhlkavKvdvMeFUEAShkv4IgQeYV864+tjj4mWHYlTueq7JoHDleCJ7110iIMSNJkGu/P5jFMd2XENro8FQOMRj56TDp7kzXYY2xaeFC/5BrmVOfhX+GETwIQiCIJSpw8AA9n8fiSRLNO/oic5Gw7VzyRhyjahAVkoe8VEZGPJMtOzsxaCpbdHbaQnp7c/1s8nk5xhx9rTFs6kTzp62IheHYCGGXQRBEIQyqarKmfAbHPk5mtzCVTD2zjY4uplzhji62+IZ4EjLzl7lZhYV/jjEsIsgCIJQY5Ik0WFAAO37NSE3y0BBrrknQxbDJUINieBDEARBuCupcF8UsTeKYC0ifBUEQRAEoU6J4EMQBEEQhDolgg9BEARBEOqUCD4EQRAEQahTIvgQBEEQBKFOieBDEARBEIQ6JYIPQRAEQRDqlAg+BEEQBEGoUyL4EARBEAShTongQxAEQRCEOiWCD0EQBEEQ6pQIPgRBEARBqFMi+BAEQRAEoU41uF1tVVUFICMjo55rIgiCIAhCZRXdt4vu43fT4IKPzMxMAJo2bVrPNREEQRAEoaoyMzNxcXG56zmSWpkQpQ4pisLNmzdRVZVmzZoRExODs7NzfVer1mRkZNC0adN7vp0g2nqvEm29N4m23ntqu52qqpKZmYm/vz+yfPdZHQ2u50OWZQICAizdN87Ozvf0P4Yif5R2gmjrvUq09d4k2nrvqc12VtTjUURMOBUEQRAEoU6J4EMQBEEQhDrVYIMPvV7P3//+d/R6fX1XpVb9UdoJoq33KtHWe5No672nIbWzwU04FQRBEATh3tZgez4EQRAEQbg3ieBDEARBEIQ6JYIPQRAEQRDqlAg+BEEQBEGoUw0u+Lh06RJjxozB09MTZ2dn7r//fvbs2VPinCNHjjB48GBcXV1xc3Nj2LBhnDhxon4qXAMVtXXVqlVIklTmIzExsR5rXnWV+b2Cuc0dO3bE1tYWX19fZs6cWQ+1rZnKtLWs3+kXX3xRTzWuvsr+XgGSk5MJCAhAkiTS0tLqtqJWUFFbk5OTGTFiBP7+/uj1epo2bcrMmTMb5T5VFbX15MmTTJo0iaZNm2JnZ0fbtm355JNP6rHG1VOZf79z5syhW7du6PV6OnfuXD8VtYLKtPX69euMHj0aBwcHPD09mT17NgUFBbVSnwYXfIwaNQqj0civv/5KREQEnTt35qGHHiI+Ph4w54wfPnw4zZo14/Dhw+zfvx9nZ2eGDx+OwWCo59pXTUVtfeyxx4iLiyvxGD58OP3798fb27uea181FbUV4MMPP2TBggW8/vrrnD17ll9++YXhw4fXY62rpzJtBVi5cmWJ3+3UqVPrqcbVV9m2AkyfPp2OHTvWQy2to6K2yrLMmDFj2LJlC5cuXWLVqlXs3r2b559/vp5rXnUVtTUiIgIvLy++/vprzp49y4IFC3jjjTf47LPP6rnmVVOZf7+qqvLUU0/x2GOP1WNNa66itppMJkaNGkV2djb79+9n3bp1bNiwgb/85S+1UyG1Abl165YKqHv37rUcy8jIUAF19+7dqqqq6pEjR1RAvX79uuWcU6dOqYB6+fLlOq9zdVWmrXdKTExUdTqdumbNmrqqplVUpq0pKSmqnZ1duW1vLCr7ewXUTZs21UMNracq/4Y///xztX///uovv/yiAmpqamod17ZmqvPfq6qq6ieffKIGBATURRWtprptffHFF9WBAwfWRRWtoqrt/Pvf/6526tSpDmtoPZVp688//6zKsqzeuHHDcs63336r6vV6NT093ep1alA9Hx4eHrRt25Y1a9aQnZ2N0Wjkyy+/xMfHh27dugEQHByMp6cnK1asoKCggNzcXFasWEFoaCiBgYH13ILKq0xb77RmzRrs7e2ZMGFCHde2ZirT1l27dqEoCjdu3KBt27YEBATw6KOPEhMTU8+1r5qq/F5nzpyJp6cnPXr04IsvvkBRlHqqdfVUtq3nzp3j//7v/1izZk2Fm001VNX57/XmzZts3LiR/v3713Fta6Y6bQVIT0/H3d29DmtaM9VtZ2NUmbYePHiQ9u3b4+/vb3nf8OHDyc/PJyIiwvqVsno4U0OxsbFqt27dVEmSVI1Go/r7+6vHjx8vcc6ZM2fUVq1aqbIsq7IsqyEhIeq1a9fqp8I1UJm2FteuXTv1hRdeqLsKWlFFbV2yZImq0+nU4OBgdfv27erBgwfVwYMHq8HBwWp+fn79VbwaKvN7ffvtt9UDBw6ox48fV//xj3+o9vb26ttvv10/Fa6Bitqal5enduzYUf3vf/+rqqqq7tmzp1H2fKhq5f97nThxompnZ6cC6ujRo9Xc3Ny6r2wNVfVv04EDB1SdTqfu3Lmz7ippBVVpZ2Pu+VDVitv6zDPPqEOHDi31PhsbG3Xt2rVWr0+dBB9///vfVeCujyNHjqiKoqgPP/ywOnLkSHX//v1qRESE+sILL6hNmjRRb968qaqqqubk5Kj33Xef+sQTT6i///67evDgQXX8+PFqaGiompOTUxfNuStrtrW4AwcOqIB69OjRemhV2azZ1sWLF6uAumPHDkv5iYmJqizL6vbt2+uriRa19Xst8o9//EN1dnauwxaVz5ptffnll9XHHnvMUnZDCz5q4/caFxennj9/Xv3hhx8a1AeG2vo3fObMGdXLy6vBBM+11c6GGHxYs63PPPOMOmzYsFLX0Ol06rfffmv1utdJevWkpCSSkpLuek7z5s357bffGDZsGKmpqSW2+23Tpg3Tp0/n9ddfZ8WKFcyfP5+4uDhLF25BQQFubm6sWLGCiRMn1mpbKmLNthY3ffp0jh07xvHjx2ul3tVhzbauXLmSp556ipiYGAICAizn+Pj4sGjRIp555plaa0dl1Nbvtchvv/1G3759iY+Px8fHx6p1ryprtrVz586cPn0aSZIA8+Q9RVHQaDQsWLCAt956q1bbUpHa/r3u37+fBx544P/bt5+XZPYwCuBPckOLSpvcBEGLqEW6aNkwi9CgTUHUsoJAggikVdAu2kjb1hX9AUFQgQUSbiJaBI1hGVIgbRIqjCDsJ5y7uCj35d73Nto4o5fzAVczyDnMII/z/Y7c3d1Je3u7qdlLVYmuqVRKAoGAzMzMSCQSqVj2UlTqmi4vL8vOzk5VvVlpZtelpSXZ3d2V8/Pz4vGnpydRFEXi8bgEAgFTs/9h6rf9htfrFa/X++15+XxeROQf68IOh6O4Hp7P58XhcBR/zArH6+rqqmLN3MyuBS8vL7K1tSUrKyvmBTWBmV01TRMRkXQ6XRw+crmcPD4+VsVenkpc17/TdV1cLpd4PJ4f5TSDmV23t7fl9fW1eOz09FRCoZAcHR1JV1eXianLU+nrWvhv9/7+/oOU5jC76+XlpQSDQZmenq6awUOk8te0mpjZVVVViUQiks1mi4NyLBYTp9NZmT0wpj9L+YGHhwe0tbVhfHwciUQC6XQaCwsLqK+vRyKRAABcXV3B6XRibm4OqVQKFxcXmJqagtvt/s/H2tXGSNeCjY0NuFwu5HI5m9L+jNGuo6Oj8Pl8OD4+RjKZxMjICHp7e/Hx8WFj+tIY6bq3t4e1tTUkk0nc3NxgfX0dLS0tmJ+ftzl9aUq5hwuqbdnFKCNdo9EoNjc3kUwmkclkEI1G4fP5oGmazelLY6RrYallcnIS2Wy2+Lm/v7c5vXFG79/r62vouo7Z2Vn09PRA13Xoul5Te9GMdP36+oLf78fg4CDOzs5weHiIjo4OhMPhimSqquED+OtV2qGhISiKgubmZvT392N/f/+Xc2KxGDRNg9vtRmtrK4LBIE5OTmxKXD4jXQFAVVVMTEzYkNA8Rro+Pz8jFArB4/FAURSMjY398kp1rfiu68HBAfr6+tDU1ITGxkb4/X6srq7i8/PTxtTlMXoPF9Tq8AF83zUej0NVVbjdbrhcLnR3d2NxcfF/2fV3ew06OzvtC10GI/fvwMDAv3bNZDL2hC6Tka63t7cYHh5GQ0MDFEVBOBzG29tbRfJYsueDiIiIqKA2X7onIiKimsXhg4iIiCzF4YOIiIgsxeGDiIiILMXhg4iIiCzF4YOIiIgsxeGDiIiILMXhg4iIiCzF4YOIiIgsxeGDiIiILMXhg4iIiCzF4YOIiIgs9Scm66rKVZp1cQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 21 unpopulated coastal tracts.  Lets plot them and their parent counties\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"We will now build the county-by-county geometries, hoping there are no islands\")\n",
    "skipList = list()  #a list of units we previously know to skip in the map\n",
    "isAlteredCounty = [False for c in range(nCounties)]\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "countyPop =       [0. for c in range(nCounties)]\n",
    "countyGeom =      [dummyPoly for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    if t not in skipList:\n",
    "        c = countyNo[t]\n",
    "        countyTractList[c].append(t)\n",
    "        countyPop[c] += tractPop[t]\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Building county geom for county\",c)\n",
    "    for t in countyTractList[c]:\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            countyGeom[c] = tractGeom[t]\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "origMAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    origMAP = origMAP.union(countyGeom[c])\n",
    "    if countyGeom[c] == dummyPoly:\n",
    "        print(\"WARNING! county\",c,\"is empty!\")\n",
    "    else:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "minTractPop = 4.5\n",
    "print(\"Here is your original county-base map before any triage; e.g eliminating coastal unpopulated tracts w pop <\",minTractPop)\n",
    "plt.show()\n",
    "if origMAP.geom_type == dummyPoly.geom_type:\n",
    "    mapExterior = origMAP.exterior\n",
    "else:\n",
    "    for i,geo in enumerate(origMAP.geoms):\n",
    "        if i == 0:\n",
    "            mapExterior = geo.exterior\n",
    "        else:\n",
    "            mapExterior = mapExterior.union(geo.exterior)\n",
    "\n",
    "for c in range(nCounties):\n",
    "    for t in countyTractList[c]:\n",
    "        if tractPop[t] < minTractPop:\n",
    "            if tractGeom[t].intersects(mapExterior):\n",
    "                isAlteredCounty[c] = True\n",
    "                skipList.append(t)\n",
    "print(\"There appear to be\",len(skipList),\"unpopulated coastal tracts.  Lets plot them and their parent counties\")\n",
    "for t in skipList:\n",
    "    plotPoly(tractGeom[t])\n",
    "    plotCenter(t,tractGeom[t],6)\n",
    "plotPoly(origMAP,0.2)\n",
    "for c in range(nCounties):\n",
    "    if isAlteredCounty[c]:\n",
    "        plotPoly(countyGeom[c])\n",
    "plt.show()\n",
    "trueMAP = origMAP  #use these terms interchangeably"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a6c93c4-af08-47d5-90f7-dc0b4513aad5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last chance to alter the skipList, otherwise the above 21 units will be axed\n",
      "here is the proposed skipList [3345, 3428, 4366, 903, 1010, 1063, 1074, 1081, 1083, 1116, 6179, 389, 4875, 2190, 2433, 2738, 4867, 4868, 5850, 247, 614]\n",
      "here is the final skipList [3345, 3428, 4366, 903, 1010, 1063, 1074, 1081, 1083, 1116, 6179, 389, 4875, 2190, 2433, 2738, 4867, 4868, 5850, 247, 614]\n"
     ]
    }
   ],
   "source": [
    "print(\"Last chance to alter the skipList, otherwise the above\",len(skipList),\"units will be axed\")\n",
    "print(\"here is the proposed skipList\", skipList)\n",
    "#skipList = [] #...  #alter here if needed\n",
    "print(\"here is the final skipList\", skipList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2989f381-466b-4882-bf1d-cfa37a7ba8ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now preserve the original county unit lists and geoms, then rebuild as needed\n",
      "removing coastal units from countyNo 4\n",
      "removing coastal units from countyNo 14\n",
      "removing coastal units from countyNo 15\n",
      "removing coastal units from countyNo 16\n",
      "removing coastal units from countyNo 21\n",
      "removing coastal units from countyNo 29\n",
      "removing coastal units from countyNo 36\n",
      "removing coastal units from countyNo 42\n",
      "removing coastal units from countyNo 51\n",
      "removing coastal units from countyNo 55\n",
      "removing coastal units from countyNo 57\n",
      "removing coastal units from countyNo 64\n",
      "Here is the revised state county map after eliminating coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We appear to have some populated islands.  Separate out the main state geom in next block.\n",
      "we will scale longitudinal distance by 0.87823\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now preserve the original county unit lists and geoms, then rebuild as needed\")\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "trueCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "for c in range(nCounties):\n",
    "    trueCountyTractList[c] = countyTractList[c].copy()\n",
    "    if isAlteredCounty[c]:\n",
    "        print(\"removing coastal units from countyNo\",c)\n",
    "        countyTractList[c] = list()\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in trueCountyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                countyTractList[c].append(t)\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            print(\"Uh oh! county\",c,\"didn't have any usable tracts\")\n",
    "MAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c])\n",
    "    plotCenter(c,countyGeom[c])\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "origPopMAP = MAP   #origPopMAP is the map after eliminating coastal unpopulated tracts, with original islands\n",
    "print(\"Here is the revised state county map after eliminating coastal tracts\")\n",
    "plt.show()\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    print(\"Excellent!  We have no populated islands.  SKIP the below code block.\")\n",
    "else:\n",
    "    print(\"We appear to have some populated islands.  Separate out the main state geom in next block.\")\n",
    "LAT = MAP.centroid.y\n",
    "xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "print(\"we will scale longitudinal distance by\",r5(xScale))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8a562a21-4107-4a3c-a320-90442a473387",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\n",
      "unit 1004 is a true island.  We will move it to adjoin the mainland in same county.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit 1641 has island pieces.  We will reduce the tract to its main state component\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RUN THIS ONLY IF WE FOUND POPULATED ISLANDS\n",
    "nSubGeoms = len(MAP.geoms)\n",
    "subGeoms = [MAP.geoms[n] for n in range(nSubGeoms)]\n",
    "subAreas = [subGeoms[n].area for n in range(nSubGeoms)]\n",
    "mainSubGeomNo = subAreas.index(np.max(subAreas))\n",
    "mainGeom = subGeoms[mainSubGeomNo]\n",
    "nonMainGeom = dummyPoly\n",
    "for geo in subGeoms:\n",
    "    if geo != mainGeom:\n",
    "        if nonMainGeom == dummyPoly:\n",
    "            nonMainGeom = geo\n",
    "        else:\n",
    "            nonMainGeom = nonMainGeom.union(geo)\n",
    "        \n",
    "print(\"Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\")\n",
    "hasIsland = [False for c in range(nCounties)]\n",
    "isIsland =  [False for t in range(nTracts)]\n",
    "islandXshift, islandYshift = [0. for t in range(nTracts)], [0. for t in range(nTracts)]\n",
    "islandList = list()\n",
    "for c in range(nCounties):\n",
    "    if countyGeom[c].intersects(nonMainGeom):  #this county contains an island.  Find all its island tracts\n",
    "        hasIsland[c] = True\n",
    "        mainCountyGeom = countyGeom[c].intersection(mainGeom)\n",
    "        plotPoly(mainCountyGeom)\n",
    "        plotCenter(c,mainCountyGeom)\n",
    "        for t in countyTractList[c]:\n",
    "            if tractGeom[t].intersects(nonMainGeom) and t not in skipList:\n",
    "                if tractGeom[t].intersects(mainCountyGeom):\n",
    "                    print(\"unit\",t,\"has island pieces.  We will reduce the tract to its main state component\")\n",
    "                    plotPoly(tractGeom[t],0.2)\n",
    "                    tractGeom[t] = tractGeom[t].intersection(mainCountyGeom)\n",
    "                    tractCP[t] = tractGeom[t].centroid\n",
    "                    tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                    plotPoly(tractGeom[t])\n",
    "                    plotCenter(t,tractGeom[t])\n",
    "                else:    \n",
    "                    isIsland[t] = True\n",
    "                    print(\"unit\",t,\"is a true island.  We will move it to adjoin the mainland in same county.\")\n",
    "                    islandList.append(t)\n",
    "                    if tractGeom[t].geom_type != dummyPoly.geom_type :  #island tract is multiple geoms.  collapse to its largest isle\n",
    "                        isleGeos = [geo for geo in tractGeom[t].geoms]\n",
    "                        isleAreas = [geo.area for geo in isleGeos]\n",
    "                        biggestIsleNo = isleAreas.index(np.max(isleAreas))\n",
    "                        tractGeom[t] = isleGeos[biggestIsleNo]\n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                    p1, p2 = nearest_points(mainCountyGeom, tractGeom[t])\n",
    "                    islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "                    plotPoly(tractGeom[t])\n",
    "                    plotCenter(t,tractGeom[t])\n",
    "                    plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                    tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                    tractCP[t] = tractGeom[t].centroid\n",
    "                    tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                    plotPoly(tractGeom[t],0.2)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9a94118d-5863-4458-b77a-67d18cf01c2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\n",
      "This is not appropriate for large islands like Michigan UP\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with island triage - RUN ONLY WITH ISLANDS\n",
    "print(\"If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\")\n",
    "print(\"This is not appropriate for large islands like Michigan UP\")\n",
    "for c in range(nCounties) :\n",
    "    if hasIsland[c] :  #rebuild the county geom with the translated islands\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in countyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "MAP = countyGeom[0]\n",
    "plotPoly(countyGeom[0])\n",
    "for c in range(1,nCounties):\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "    plotPoly(countyGeom[c])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4aebe4ee-ee7b-4bf3-8413-f315556d453e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing all county centerpoints, then plot them\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"computing all county centerpoints, then plot them\")\n",
    "countyCP  = list()\n",
    "cutCountyList = list()\n",
    "for c in range(nCounties):\n",
    "    cTL = countyTractList[c]\n",
    "    countyCP.append(getHDcp( [tractCP[v] for v in cTL], [tractPop[v] for v in cTL], [i for i in range(len(cTL) ) ] ) )\n",
    "    if countyCP[c].disjoint(countyGeom[c]):  #possible with weird-shaped county\n",
    "        countyCP[c] = nearest_points(countyGeom[c],countyCP[c])[0]\n",
    "    plotPoly(countyCP[c].buffer(0.03))\n",
    "    plotPoly(countyGeom[c],0.5)\n",
    "plotPoly(MAP)\n",
    "plt.show()   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "207f3a55-e361-4777-ae46-41fe3d54a4b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code block identifies whole districts to be cut out of the map\n",
      "For FL my input file says to cut out 2 districts out of 28\n",
      "performing cut no 0 for state FL\n",
      "the total proposed cutPop is 766222.0 . Compare to 769220\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to apply the cut 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "performing cut no 1 for state FL\n",
      "the total proposed cutPop is 768494.0 . Compare to 769220\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to apply the cut 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your cut districts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# updated NCUTDISTRICTS (district slice-out) code vs. vanillaRefine - this WORKS\n",
    "print(\"This code block identifies whole districts to be cut out of the map\")\n",
    "unclippedMAP = MAP\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "nCutDistricts = nCuts\n",
    "nStops = trimDF[\"nStopLines\"][DFrow]\n",
    "nDistricts = trimDF[\"nDistricts\"][DFrow]\n",
    "stopLines = list()  #the 'stoplines' stop wedge growth that hops across concave map areas (not currently used)\n",
    "cutCountyList = list()\n",
    "allCutLists, allCutPops = list(), list()\n",
    "print(\"For\",STATE,\"my input file says to cut out\",nCuts,\"districts out of\",nDistricts)\n",
    "if nCuts > 0:\n",
    "    cutList = list()\n",
    "    cutXs = ast.literal_eval(trimDF[\"cutXs\"][DFrow])\n",
    "    cutYs = ast.literal_eval(trimDF[\"cutYs\"][DFrow])\n",
    "    cutPoints = [Point(cutXs[i],cutYs[i]) for i in range(len(cutXs)) ]\n",
    "    for cutNo in range(nCuts):\n",
    "        print(\"performing cut no\",cutNo,\"for state\",STATE)  #first two cutPoints must form the cutLine\n",
    "        cutNotDone = True\n",
    "        thisCutPoints = [ cutPoints[int(4*cutNo)],cutPoints[int(4*cutNo+1)],cutPoints[int(4*cutNo+2)],cutPoints[int(4*cutNo+3)]  ]\n",
    "        while cutNotDone:\n",
    "            cutPoly = Polygon([thisCutPoints[0],thisCutPoints[1],thisCutPoints[2],thisCutPoints[3] ])\n",
    "            cutLine = LineString([thisCutPoints[0], thisCutPoints[1] ] )\n",
    "            cutList = list()\n",
    "            cutPop = 0.\n",
    "            for c in range(nCounties):\n",
    "                if cutPoly.intersects(countyGeom[c]):\n",
    "                    if cutPoly.contains(countyGeom[c]):\n",
    "                        cutList = cutList + countyTractList[c]\n",
    "                        cutPop += countyPop[c]\n",
    "                    else:\n",
    "                        for t in countyTractList[c]:\n",
    "                            if cutPoly.contains(tractCP[t]):\n",
    "                                cutList.append(t)\n",
    "                                cutPop += tractPop[t]\n",
    "            print(\"the total proposed cutPop is\",cutPop,\". Compare to\",int(statePop/nDistricts) )\n",
    "\n",
    "            doIcut = input(\"enter 1 to apply the cut\")\n",
    "            if str(doIcut) == str(1):\n",
    "                cutNotDone = False\n",
    "                allCutLists.append(cutList)\n",
    "                allCutPops.append(cutPop)\n",
    "            else:\n",
    "                print(\"Here is the state and the last proposed cutPoly.\")\n",
    "                plotPoly(cutPoly)\n",
    "                plotPoly(MAP)\n",
    "                plt.show()\n",
    "                print(cutPoly)\n",
    "                oldPts = list(cutPoly.exterior.coords)\n",
    "                newPtXs = ast.literal_eval(input(\"enter alternate [ x0, x1 ] for the first two points\") )\n",
    "                newPtYs = ast.literal_eval(input(\"enter alternate [ y0, y1 ] for the first two points\") )\n",
    "                thisCutPoints = [Point(newPtXs[0],newPtYs[0]), Point(newPtXs[1],newPtYs[1]),oldPts[2], oldPts[3] ]\n",
    "allCutVTDs = list()\n",
    "for L in allCutLists:\n",
    "    for v in L:\n",
    "        allCutVTDs.append(v)\n",
    "print(\"here are your cut districts\")\n",
    "plotPoly(MAP)\n",
    "for i,L in enumerate(allCutLists):\n",
    "    cutGeo = tractGeom[L[0]]\n",
    "    for v in L:\n",
    "        cutGeo=cutGeo.union(tractGeom[v])\n",
    "    plotPoly(cutGeo,2)\n",
    "    plotCenter(i,cutGeo)\n",
    "    plotCenter(allCutPops[i],Point(cutGeo.centroid.x,cutGeo.centroid.y-0.5) )\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "4c915415-6c44-4c43-9826-b565cebed95a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Write the vtd cutlists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Write the vtd cutlists to a file\")\n",
    "cutDistrictNo = list()\n",
    "for v in allCutVTDs:\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        if v in L:\n",
    "            cutDistrictNo.append(i)\n",
    "            break\n",
    "nbrDF = pd.DataFrame( {\"vtdNo\":allCutVTDs,\"cutDistrictNo\":cutDistrictNo} )\n",
    "outname = STATE+\"wholeDistrictCuts.csv\" \n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "nbrDF.to_csv(outpath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "17721728-e5c0-4787-b430-9d7d94970667",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "countyPop = [0. for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    countyPop[countyNo[t]] += tractPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "02f95c46-5073-4b9d-817c-16a57d5ebb52",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28\n"
     ]
    }
   ],
   "source": [
    "print(nDistricts)\n",
    "#nDistricts = 28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f3073320-8d92-45ba-beb2-fb8c08d7170d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed all whole districts.  Finalize stats before skewing outcroppings.\n",
      "Of the total statePop 21538187.0 we ignore 1534716.0 as it is cut out of the map\n",
      "This excluded pop comes from a total of 791 tracts\n",
      "Our final adjusted number of state districts, excluded fixed cut-out is 26 rather than original 28\n",
      "Each district will be drawn to house 769364.269 = 1/ 26 of non-cutout state pop 20003471.0 vs original= 21538187.0\n",
      "Here is a county-level modified map with each county's fraction of a district pop\n",
      "working on county 0\n",
      "working on county 20\n",
      "working on county 40\n",
      "working on county 60\n",
      "here is the FL map excluding cut and unpop coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Removed all whole districts.  Finalize stats before skewing outcroppings.\")\n",
    "trueCountyPop = [countyPop[c] for c in range(nCounties)]\n",
    "trueCountyGeom = countyGeom.copy()\n",
    "countyPop = [0. for c in range(nCounties)]\n",
    "trueStatePop, true_nDistricts = np.sum(trueCountyPop), nDistricts\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "# minTractPop = 4.5  #already defined above\n",
    "populatedTractList = list()\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "statePop, excludedPop = 0., 0.\n",
    "\n",
    "for t in range(nTracts):\n",
    "    trueCountyTractList[countyNo[t]].append(t)\n",
    "    if t in allCutVTDs or t in skipList:  #  or vtdPop[t] < minTractPop:  #need to keep low-pop vtds as VEST may have assigned votes to them\n",
    "        excludedPop += tractPop[t]\n",
    "    else:\n",
    "        populatedTractList.append(t)\n",
    "        countyTractList[countyNo[t]].append(t)\n",
    "        countyPop[countyNo[t]] += tractPop[t]\n",
    "        statePop += tractPop[t]\n",
    "        #tractPop[t] = max(tractPop[t],0.000001)  #avoid possible later div/zero errors for nil-vote vtds\n",
    "print(\"Of the total statePop\",statePop+excludedPop,\"we ignore\",excludedPop,\"as it is cut out of the map\") #,\n",
    "#\"or in sparsely populated areas (<\",minTractPop,\") per geom\")\n",
    "print(\"This excluded pop comes from a total of\",nTracts -len(populatedTractList),\"tracts\")\n",
    "true_nDistricts = nDistricts\n",
    "nDistricts -= nCutDistricts\n",
    "print(\"Our final adjusted number of state districts, excluded fixed cut-out is\",nDistricts,\"rather than original\",true_nDistricts)\n",
    "aDP = statePop/float(nDistricts)\n",
    "print(\"Each district will be drawn to house\",r3(aDP),\"= 1/\",nDistricts,\"of non-cutout state pop\",statePop,\"vs original=\",trueStatePop)\n",
    "print(\"Here is a county-level modified map with each county's fraction of a district pop\")\n",
    "\n",
    "vtdGeom = tractGeom.copy()\n",
    "nVTDs = len(vtdGeom)\n",
    "\n",
    "cutCountyList, uncutCountyList = list(), list()\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"working on county\",c)\n",
    "    if len(countyTractList[c]) == 0:  #no vtds added to county b/c all were in cut lists:\n",
    "        cutCountyList.append(c)\n",
    "    else:\n",
    "        uncutCountyList.append(c)\n",
    "        countyGeom[c] = tractGeom[countyTractList[c][0]]\n",
    "        for t in countyTractList[c]:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "uncutMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "print(\"here is the\",STATE,\"map excluding cut and unpop coastal tracts\")\n",
    "\n",
    "for c in uncutCountyList:\n",
    "    plotPoly(countyGeom[c])\n",
    "    FONTSIZE = 12\n",
    "    if countyPop[c] / statePop < 0.02:\n",
    "        FONTSIZE = 7\n",
    "        plotCenter(r3(countyPop[c]/aDP),countyGeom[c], FONTSIZE)\n",
    "    else:\n",
    "        plotCenter(round(countyPop[c]/aDP,2),countyGeom[c], FONTSIZE)\n",
    "plotPoly(trueMAP,0.1)\n",
    "for c in cutCountyList:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "    plotCenter(\"cut\",countyGeom[c],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0062ac21-3328-4616-99ed-70c53e62b308",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the map topology before any squish operations.  Plot countyPop/aDP\n",
      "Working on county  0 distances\n",
      "Working on county  20 distances\n",
      "Working on county  40 distances\n",
      "Working on county  60 distances\n",
      "the max LAT-scaled distance between counties is 5.98639\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter user-picked max district diameter, or 0 to accept 5.98639  4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on county  0 neighbors\n",
      "Working on county  20 neighbors\n",
      "Working on county  40 neighbors\n",
      "Working on county  60 neighbors\n",
      "I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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cnoVhKDaUNKAfnSbcnWBh9rBUrGYJHaJ3xuVE/uDag2GeX1vCp2cVRuV1DEMx1tYMpER19M368hZ2LdtG65YDpITNmAjg+sSFpBZ6UEqRmnP2SxnRYLWbz7lRqNliIm9sKnljr0Ipxd7qZlr8IYyQwggajM1IIhQwCAXDR/9rEGgL4bGOk+AhRJQMqb8sw1D8bc1hQoZicn4yY7NcJFgHVsNGMThoGjii1OBUKcXie/7ORcn5vNtYzqf6+AuyobKFHa9to23LfpINM2EVggwnrvnnMeW8bExxHNI1TWNUprPj/saSBpJS5PKpEP1tSIUPXdf4zOwiAIJhg71VzfhDYcKGisvxO8TAZdajMx7Jkd117PnVa1yUXMi7jeXc+ptb+mS7zQ1+di7fTeOHO0gOaRgqjJblxrNgGgUT0zEPsN5HfcVmNtEeDA+43lVCDHZDKnycyGLSO06Rf7CvNsbViMFm6dYK5gzv+8aK7//8Tea4C/nQW8JtT32qV5c82luC7H7vIFUrNuNuMwgrAzyJuK+cxtRp2ZiHwFnBYekODtS0dHwWCCH6x5AMH6GwwVu7qsl02SNtPhKlS53oWxeOSOON7ZXcOrOgz7YZDISZ484DYNoXLulR8Aj6w+xfe4SSN9fjbGyPjBWSaCbx2umMuqCwz+aKiRd2iwlfe5CSulYKUqPfaFcIETG0PmmOMpt0xmW7KGtoY0ZRigwaJvpcisPKZWMy+MfaUuaNyyQl0dLrcTjMFp33Gsu5MDmH3R/VUjw195yeFw4bHN5SzYFX12Gv8mHVTGhWhfXSiYy5bCQOd/910x2Izh+WyrIdVeR7EmQGWyH6yZAMHwD5nkRykhNYe6i+04RxQvSVDJedm6blsbPCx5oDdRSnO8hNTqA1EMbXFqQlEAagtslPapIVBbQHwhyobeHSMRmYdY2QofAkWqnwtrFj8TqGH80JBRPSz/jaylAc2dvAnlfWYzpcjUOzgB7CNGMUoxeMw50uv/JPlO60SfAQoh8N2fABkaG8HVazNDgTUWPSNSbkupmQ66apPUh1kx+H1Ux2cgJJtq7//KYXeWhsDRAyFBZdY3u5l5GZTuwlVdQ5LIz62jTyuhjXQylFbVkzO1/ZRGhXCW7NCiqANq6AomumkJqbJF+wQogBYUiHD4DxOS42ljYyrTAl1qWIQc5pt+C0n719kdWsk3HC6LnH/62wumynDCjmrWllx+vbadmwl2RlwTACaIUZZF47jfNGpMRkHI54M8DGWhRi0Bvy4UPXNZITLaw/3ECG00a+R05HiwFKKTQt0j6pxetn19t7qf9gG8kBjbAKQ6YLz8emM3VSRr+ONjoYZLjslDe2kdNPQ+MLMdQN+fABMDw9MrFVla+d9YcbABiZmYTrHH6lCtFflAH1hxp48/4XcLeGMJSCZDvO66cxZUYuliHQNTZacpMTWH+4XsKHEP1EwscJMl12Ml12lFLsq25mb1UzWW57v00UJsQZOWyMRqdCBUm46jxGXVgsM6/2oeREKzVNftKdQ7v3jxD9YUiGjze3V57TjLRmXSMQMvqhIiHO7sLvXks4ZDBxiHeNjZbh6UmsP1xPeWPbadcxlGJqgbQPE6K3hmT4yHTZmZyfHOsyhOgWOcsRfdMKzzzNQmNrgB3lPhkRVYheGpKt0qRduxCiJ5ITrSgUja2BWJciRFwbkuFDCCF6anyOmx3lPumeK0QvSPgQQohumlqQwqbSxliXIUTcGnLhwzAUMuSSEKI3Eqwm3AkWKrynb5wqhDi9IRc+QobCbJL4IYTonWHpSRyuayVsyOUXIbpryIWPsKEw60PubQshomBGkYcNJQ2xLkOIuDPkvoXDSmGSuS6EEH3ApGukJdmo9rXHuhQh4srQCx9hCR9CiL5TnObgYG1LrMsQIq50K3z89re/ZdKkSbhcLlwuF7Nnz+a1117reFwpxUMPPUROTg4JCQlccsklbN++vc+L7o2QYWCW8CGE6ENjc1xsL/fGugwh4ka3wkdeXh6PPvoo69atY926dVx22WVcd911HQHj8ccf56c//Sm//vWvWbt2LVlZWVxxxRU0NTVFpfieCBty5kMI0bdcdgvBsKItEI51KULEBU31cqQcj8fDE088wV133UVOTg5f//rX+c53vgOA3+8nMzOTxx57jC9+8YvntD2fz4fb7cbr9eJy9f0Qxkca27CYNDKcZ5/bRQghzpVSijUH6zl/WGqsSxEiJrrz/d3jNh/hcJjFixfT0tLC7NmzOXjwIJWVlcyfP79jHZvNxsUXX8wHH3xw2u34/X58Pl+nWzQFQwYW6e0ihOhjmqaRl5JAaX1rrEsRYsDr9rfw1q1bSUpKwmaz8aUvfYkXX3yRcePGUVlZCUBmZman9TMzMzse68qiRYtwu90dt/z8/O6W1C0hw5BxPoQQUZGXkkhdSwBvazDWpQgxoHU7fIwePZpNmzbx4Ycf8uUvf5k77riDHTt2dDyuaZ2/2JVSpyw70f3334/X6+24lZaWdrekbgmGFRaTnPkQQkTHlPxkdlb6pP2HEGdg7u4TrFYrI0aMAGD69OmsXbuWX/ziFx3tPCorK8nOzu5Yv7q6+pSzISey2WzYbLbultFjobCS3i5CiKiaVezhg/11TCtMwW4xxbocIQacXp8CUErh9/spLi4mKyuLZcuWdTwWCARYuXIlc+bM6e3L9BkZZEwIEW2apjFneCrby70caZT5X4Q4WbfOfHzve9/jqquuIj8/n6amJhYvXsyKFSt4/fXX0TSNr3/96zzyyCOMHDmSkSNH8sgjj5CYmMinPvWpaNXfbWe7DCSEEH1B0zSmFXrYWNKA027GZbfEuiQhBoxuhY+qqio+85nPUFFRgdvtZtKkSbz++utcccUVAHz729+mra2Ne+65h4aGBmbNmsWbb76J0+mMSvE9kWA1sfnoVNiZLjtZbulyK4SInqkFKby3t4YLhqehy1lXIYA+GOejr0V7nI8TbS5tZHJ+clRfQwghgmGDLWWNTCv0xLoUIaKmX8b5EEIIcW4sJh1dLvcK0aHbvV0GEwUYhsJQCgVogFm64QohokDChxDHDelvWotJY1u5l50VTeyubOLNHVWxLkkIMUgVeBLZWiaTzwkBQ/zMx/gcd6f7VrPOhpIGPIlWClMTpVeMEKLPpDisVPraqWv2k5rUf2MbCTEQDenwcbJRmZFeOXXNfjYe7REDMCzNQXKiNUZVCSEGi7HZLlbvr2NGgkUu8YohTcJHF1KTbB2/TJRSHKxt4UBtCwCeRCtFaY5YlieEiGOzij2s2lfLrGEebGYZ/VQMTRK9z0LTNIalJ3FeQQrnFaRQ6WuPdUlCiDim6xoXjkxjw+FG6lsCsS5HiJiQ8NFNMk+DEKK3NE1j9vBUjjS0US7Dr4shSMKHEELEyMQ8N+WNbbQHZQZcMbRImw8hhOgnja0BAmEjMsgQkf/kpSTy+rZKrpmULY1QxZAhR3o3BcNGrEsQQsSprUe8lDe2EzIUoaMDHALMHp4qXfvFkCJnPrpJKaht9pMm/fSFEN104ch0th2JDDSWk5wQ42qEiB0589FNVrMuwUMI0WMTct1424KU1rfGuhQhYkbCRzdpgD8kjcOEED03NtuFPxTm4NHxg4QYaiR8dJPNomNIsw8hRC+NyHCiAfuqm2JdihD9TsJHN9nMJgIhSR9CiN4rSnNgM5vYWeGLdSlC9CsJH93ksJlo8gc7LTMMRW2zn40lDWwsaeDVrRUxqk4IEW/yPYmkJFrZdMJ8UkIMdtLbpZvSk2ysPlBHXXPgWFd9NMDjsDIlPxlN09DkQ0QI0Q1Zbjt2i87q/XWcP8wj3W7FoCfho5s0TWPO8LRYlyGEGGSSE61MLUhm1b5aZg9LlQHHxKAmR3cUyG8WIURP2C0m5gxP48MD9bQGQrEuR4iokfARBersqwghRJdMusYFI1LZWdEkY4GIQUvCRx8LhQ28bcGzryiEEKehaRrTClMIG4rN0oZMDELS5qMPlda3UuFtZ+4IaRMihOi9ojQHvvYgq/fXkWA1nfK4Uookm5mRmc4YVCdEz0n46CNhQ1FS38oFEjyEEH3IZbcwe3jqaR+v9Laz7lA90wpTpJeMiBsSPnohbCh2VzbRFgyjazCjyBPrkoQQQ0yW2447wcKqfbXMKPJgt5x6hkSIgUbCRzcopThc10pDawAAXdMYlens8nSoEEL0lwSribkj0lh7qIGitEQynPZYlyTEGUn4OIvaZj8l9a1oRHqxFHoSKUpzxLosIYToRNM0ZhZ72Fnhw9cWZESGtAMRA5eEj5O0BcLsrmrCUJEOs6kOK1OPjlwqhBAD3dhsF0ca21h/uIFphSmxLkeILg358HGs3UZ7KAxAgsXExFw3Jl3ChhAiPuUmJ+BOsPDe3hpmFadiNcuoCmJgGbLh451d1bgTLZik3YYQYhBKspm5YHgaaw7WMyozidQkW6xLEqLDkA0fw9OTsFt0Mlzda5gVDBvUNvupbQoQVqeOZapOWHbipZpjbUYArCad3OQEXAlmuZwjhIgaXdeYPTyVbUe8+NpDFEt7NTFADNnwUZCayIcH6jrChz8UpqbJT31LAONoSjgWC06MGGZdIy3JxphsJ5YeTvzUHgxT4W3nYF1LZPtKYSiYkp8sl3uEEH1uQq6b0vpWNpc2Mjk/OdblCDF0wwfAyIwkNpU2ogEWk06608b4nOi397BbTJ1+gYTCBu/vryNsKAkfQoioyPck4rJb+GBfLbOHp8pZVxFTQzp8pCbZBsR10Pf21nLRqHQJHkKIqHInWphWlMJ7e2uZOyINXT5zRIxIE+gBwOOwUt3UHusyhBBDgM1sYvbwVN7fX0trIBTrcsQQJeFjAMhJTqC2KRDrMoQQQ4TFpDN3RBrby31U++SHj+h/Q/qyy0CR7rRxpLGtR8/dW9WErz3U6ZLNiT1rjt3n6DKzrmE169jMOlazTqLFLL1uhBiCNE1jRpGH3ZVNVDf5mZDrjnVJYgiR8BFjobDBwdoWKr3tkH9uzzEMxdYjXoJhgxEZSd2aTjsUNgiEDfxBA3/IoK7F39Hr5kRpSVbyUhLPebtCiPg0OsuJtzXI6v11DM9wyLwwol9I+IixvdXN+EMGCyZknXXd9mCYbUe8aFqk65zN3P2B0cwmHbNJJ9F6+nXKG9to8cu1YCGGCneihdnDUzlY28LaunoKPYndHgNJiO6QNh8xNjbbRbrTxpoDddQ1+8+47oajczVMK/T0KHici+3lXtqC4W6dTRFCDA7FaQ5mFHloC4ZZd6ieKmkPIqJEznwMALnJCeS47ZTWt1FS34DeRfsLQynG57ij3jZDKUhOsET1NYQQA1thqoPCVAeHaltYc6COyfnJ2C0yBYXoO90687Fo0SJmzJiB0+kkIyOD66+/nt27d3dap6qqijvvvJOcnBwSExNZsGABe/fu7dOiByNN0yhITWRqQQqT85NPuU0tSMGdGP1QMCHXTaWvnQ0lDRjGqcPHCyGGjqI0BzOLPeyqbGLbEW+syxGDSLfCx8qVK1m4cCEffvghy5YtIxQKMX/+fFpajg8Tfv3113PgwAFefvllNm7cSGFhIfPmzetYRwx843PcTMhxs7mskQ0lDWwsaaCpPRjrsoQQMaBpGlPykylKc/DRwfoe98wT4kSaUl3MjnaOampqyMjIYOXKlVx00UXs2bOH0aNHs23bNsaPHw9AOBwmIyODxx57jM9//vNn3abP58PtduP1enG5XD0tTfQhpRT7qpup8vmZWeyR6bmFGMJK61spb2xjUl6yzAYuOunO93evvkW83shpOI/HA4DfH2kwabcfbyVtMpmwWq2sWrWqy234/X58Pl+nmxhYNE1jZKYTu0VChxBDXb4nkZnFHvZWN7GlrJFe/H4VQ1iPG5wqpbjvvvuYO3cuEyZMAGDMmDEUFhZy//3387vf/Q6Hw8FPf/pTKisrqaio6HI7ixYt4oc//GFPyxhwlFIs31mNO8HScYbgxCaix/5MB+qyM6lp8vP8RyWMznJy/rDUc3yWEGKw0TSNSXnJtAZCrD3UQLbbTr5HxgUS567Hl10WLlzI0qVLWbVqFXl5eR3L169fz+c+9zk2b96MyWRi3rx56HrkS/jVV189ZTt+v7/jjAlETtvk5+fH9WWXUNhgT1Uz/lC4Y1mGy06O2x713iretiBWky6nQ4UQ/eZIYxtl9a1MyHXjsEknyqGqO5ddenSU3HvvvSxZsoR33323U/AAmDZtGps2bcLr9RIIBEhPT2fWrFlMnz69y23ZbDZsttjPLNuXzCadcTnHd7xSippmP5vLvKeceTjT/WOOdb01lOJMSfFYjnTaLYzISOph9UII0T3HhgvYXu4jZCgm50V/WAAR37oVPpRS3Hvvvbz44ousWLGC4uLi067rdkfmCdi7dy/r1q3jRz/6Ue8qjWOappHhtPdo2GKlFEpFgonpHKa/9rYGqW0582BlQgjR1zRNY0Kum/ZgmLWHGshy2SlIlUsxomvdakG4cOFC/vrXv/Lcc8/hdDqprKyksrKStrbjXa/++c9/smLFio7utldccQXXX3898+fP7/PihwJN09B17ZyCB0SGSa5pkvAhhIgNu8XEzGIPFrPGmgN10k1fdKlbbT5OdxrtmWee4c477wTgl7/8JU888QRVVVVkZ2dz++2388ADD2C1nmEykRNIV9vea2gJcLCuhfMKUmJdihBiiNt2xEsgbDA1P1kuxQxy3fn+7tU4H9Eg4aNvNLYG2FHhY1JeMknSAEwIEUPtwTBbyrykJVkZli7t0QarfhvnQwxcyYlWZg9LZe2herxtctpTCBE7xy7FJNnNfHSwXi4NCwkfg1kgbOCwmnHLRHFCiAEgw2lnZrEHX3uQjw7W0x4Mn/1JYlCS8/GD2LYjPqYXSrsPIcTAMjw9iWFpqmP4gUnSNXfIkfAxiJn0SE8ZIYToLW9bkLrmzpdLTgwMWqflnZ+rnfDoiY+lOqz4Q2Fe2nSE0ZmuTuMjicFNwscgppQiFDYwm+TqmhCid/zBMDVNfsYeDQiduiqc1G3hxOEQT1zv5N4NSikSrSYuGpmORSasHFIkfAxik/KSWXOgjjkj0mJdihAizmW47JQ2tGI16dgtMn2D6B2JmoOYSdcYn+Pmg/21GMaA6lEthIhDU/NT+OhgvXyeiF6T8DHIuRMtzCjysPpAHQ0tgViXI4SIY7quMXt4KutLGmJdiohzEj6GAItJ54IRaVQ3+dlR7ot1OUKIOGYx6aQn2ShvbDv7ykKchoSPIWR0lhMAn8y1IITohaI0B7sqfQywAbJFHJHwMcSMy3Gxrcwb6zKEEHFuzvA0Vh+oi3UZIk5J+BiCRmc52VfdFOsyhBBxzG4xoWsazf5QrEsRcUjCxxCUmmSjLWBQ3dQe61KEEHEsLyWBD/fX8Yf3DrCrUtqTiXMn43wMURPz3Gwv91LbFJBRBYUQPZKXkkhako0Uh4UxWfI5Is6dnPkYwsbnuMlNTuCjg/XsrZLLMEKI7rNbTGQ47XIpV3SLhI8hzp1oYWaxh7qWAKX1rbEuRwgRh/I9idgtJrb2Y2N2pRSbShv77fVE35LwIdhe7qU4zUG+JzHWpQgh4lReSiK5KQl8sL+WcD+MgKppGu/vq43664jokDYfQ5yvPYiuaWS67LEuRQgR5zwOKzOLPHx0sJ5Ea8/nf1FEZslNTbKSlxL5UaSU4p/rysjzJOCwRr66cpMTePrdA9x90bA+qF70JwkfQ9z+6mam5CfHugwhxCBhNunMHp7aJ9uqbmpn4wlDuU/OT+alTUe4fkouo7OcTM5PprE1wOr9dcwq9qDrWp+8rog+CR9DnEnXCBsKs0n+aIUQA0uG006G8/hZ2ab2IJePyaAo7fgl4uREK9MKU/jwQB1jsl14HNZYlCq6Sdp8DHHtQYPWYDjWZQghxFk57RamF3mwmTtf0rGadeaMSKPC28Ye6bkXFyR8DFFKKT7YX8vYbCcuuyXW5QghRK+0BcKYdI29Vc0yf1UckMsuQ9TG0kbOK0jBbul5ozAhhIi1ZTuqqPC2YTebKEhNJC8lgQM1LUCk0WrIUEwrTIltkeIUEj6GoLZAGF3TJHgIIeLeFeMyT/tYMGywpayx/4oR50zCxxATDBusO1zP3BFpsS5FCCGiotLbzpHGViwmncl5ybEuR3RBwscQopRi9f465o5IQ9Okd4sQYvDxh8IcaWxjWqEn1qWIM5DwMci1BcIcqmvBHzJo9YeYKX3hhRCD2KaSRmYWS/AY6CR8DCLtwTB7qpoIG6rjzEai1URxmkPadwghBr3DdS3kexLlzG4ckPAR59oCYXZW+gCwmnTGZbswm6QHtRBi6Klt9svlljgh4SNO1bcE2F/TTILFxNT8ZEn6Qoghra7ZT6rDFusyxDmS8BFnlFJsKGnAabcwo0gSvhBCAByqa5XxPOKIhI84YhiKVftqmVnskTYcQghxAkMpwobCJA3q44I0DogjG0oamD08VYKHEEKcZGp+cqcZcMXAJuEjThhHE71FGpMKIcQpzCYdj8NKta891qWIcyDfZHGiORDCKRPACSHEaQ1LT+JQXWusyxDnQNp8xIkkq4lVu3fTVG7CUMYpjx9bplAdy5RSnZYp1AnLjq1j4Pf72b53JyOKhuNwJKKOPvfYmoZSJzz3+H3Qjq6njv8XxdGXwFDGCduKLByWXsB546b05a4RQogOIzKS2FfdxIgMZ6xLEWcg4SNONDU18UjbfmytAfSjQUM7IWgc+2fnplbqHJfBxqILOK9tA7R13q529DkaoKljz9dOWEd12pZ2Ql1aR02RfwR0MwlNR/i7hA8hRJR4HFb21zSjlJIhCAYwCR9xwuVyMa61khKbi8RwO7o6HiI0Tvw3aEp1/Jujj2sACjSt87W2YxFifnAVAGal+ELBLHLTsk95fbfb3av38Obqt/hVw0H5UBBCRNXEXDdbyrxMzk+OdSniNCR8xAlN0/ivrPP4TdVGwpqOQsPQdJSmYWgaCh1Diywz0CLL0SPrcGy53rE8oFnJClQd23pHCKmzeLi+0QqNdZ1e/4KWpfzrmi/16j2k2lNo148QDAaxWq292pYQQpyO3WLCUAp/KIzNLL0DByIJH3EkSIiyxGyu9TdgMZnQNQ0NDR0NXTt209HVifc1NE1HR8OkRdZGKfY2lmCyWo4+HrmQYtI0CNbT5i2LnCVBw6QBaFw2cmav609KdGBoOn6/X8KHECKqJucls76kQQZjHKAkfMSRKSMnYl33BlVNjTgcSb3bmFkHIwyArml8dvKVFBcU90GVp3cscAQCgai+jhBC6LqGy26hsTVAcqL82BlouhU+Fi1axL///W927dpFQkICc+bM4bHHHmP06NEd6zQ3N/Pd736Xl156ibq6OoqKivjqV7/Kl7/85T4vfqjxeDzcHA6yx2alKeSno/0n6tj/HXVCj5ZIdxMA1vzzDda9sKzTNhPcSVz4/K8IvPV3EksDvPvuu2zatIm6ujo2btzIlClTzljT008/zbPPPsu2bdsAmDZtGo888ggzZ556psRiiXQVDgaD3X/zQgjRTaOznHx4oI7RmU5SHBJABpJuhY+VK1eycOFCZsyYQSgU4vvf/z7z589nx44dOBwOAP77v/+bd955h7/+9a8UFRXx5ptvcs8995CTk8N1110XlTcxVGiaxsTMEfyr9lBHj5fuqLM5cBTmMvWR7xzfpq5TYitgc9EU2va8wtR0O48++ih33333OW1zxYoV3HbbbcyZMwe73c7jjz/O/Pnz2b59O7m5uZ3W1XUdTR3v7iuEENF2/rBUth3xUtcSYERGL88Yiz7TrfDx+uuvd7r/zDPPkJGRwfr167nooosAWL16NXfccQeXXHIJAF/4whf43e9+x7p16yR89IGJw8aRWLWLGZUNfHLONWia1ukGnHbZz9M38KZjD89fdMMp6xmGQUvuJygo+CaVlZXnXM/f/va3TveffvppXnjhBd566y1uv/32To8dq8Uwuh+chBCipybkuqnytbPmQB0zijzoMv9LzPWqzYfX6wUilwOOmTt3LkuWLOGuu+4iJyeHFStWsGfPHn7xi190uQ2/34/f7++47/P5elPSoJeSksLcUJiN6YmMGTOmW891u90cOnSImTNnYrPZmDVrFo888gjDhg3rs/paW1sJBoOdjoljdD3SyVfChxCiv2W67KQ6rHx0qJ4RGUmkJdliXdKQ1uPh1ZVS3HfffcydO5cJEyZ0LP/lL3/JuHHjyMvLw2q1smDBAp566inmzp3b5XYWLVqE2+3uuOXn5/e0pCHDis6pw4Sd3axZs3j22Wd54403ePrpp6msrGTOnDnU1dWd/cnn6Lvf/S65ubnMmzfvlMeOhQ+57CKEiAWzSef8YanUNQfYWSE/dGOpx+HjK1/5Clu2bOH555/vtPyXv/wlH374IUuWLGH9+vX85Cc/4Z577mH58uVdbuf+++/H6/V23EpLS3ta0pCwetNHvGR3cnvq8G4/96qrruKmm25i4sSJzJs3j6VLlwLwl7/8pU9qe/zxx3n++ef597//jd1uP+VxOfMhhBgIRmc5yXDa+GBfLYGQfB7FQo8uu9x7770sWbKEd999l7y8vI7lbW1tfO973+PFF1/k6quvBmDSpEls2rSJJ598sstfwzabDZtNTn+di7a2Np4s28CCsMbHrvhEr7fncDiYOHEie/fu7fW2nnzySR555BGWL1/OpEmTulxH0zRCmpn/2/QqmXuSSbU5SUtIJs3hIS0tDY/H09EjRgghoik1ycb5w6ysPVRPvieRnOSEWJc0pHQrfCiluPfee3nxxRdZsWIFxcWdx4UIBoMEg8GOX7jHmEwm+bXbB/6xagntuoV7L/5EnwxP7vf72blzJxdeeGGvtvPEE0/w8MMP88YbbzB9+vTTrudyubg60MxBk8FefwOtAR+tLTX4G6xQBvawn8lhPzcVnc/U8VNOOY6EEKIv6brGrGGp7K9pZnNpowzH3o+6FT4WLlzIc889x8svv4zT6ezoFeF2u0lISMDlcnHxxRfzrW99i4SEBAoLC1m5ciXPPvssP/3pT6PyBoaKYDDIEn8D11mcOJ09m63xm9/8Jtdeey0FBQVUV1fz8MMP4/P5uOOOOwCor6+npKSE8vJyAHbv3g1AVlYWWVlZANx+++3k5uayaNEiIHKp5YEHHuC5556jqKio45hISkoiKalztzaz2cy3ru3chdcwDFpbW6mrq2N72W7eqNvNfRW7mbFvFQ/O+2yP36sQQpyr4elJ+NqDvL+vlvMKUkiwypDsUae6gaNDVp18e+aZZzrWqaioUHfeeafKyclRdrtdjR49Wv3kJz9RhmGc02t4vV4FKK/X253SBr2dO3eqi19/TlVXV/d4G5/85CdVdna2slgsKicnR914441q+/btHY8/88wzXf7v++CDD3asc/HFF6s77rij435hYeFZn9Nde/fvVdcv/b36nyW/7fE2hBCiuwzDUOsO1av91U2xLiUudef7W1NqYHU98Pl8uN1uvF4vLpcr1uUMGCs/eo9H6/bxyvzbMZkGfyr/xzsv8feWKv51zRdjXYoQYoip8LZR1tDG9MIUmYG7G7rz/S1zu8SJnJQs/A0lNDY2kpqaGutyoq7AlU1zeyPhcHhIhC0hRP/7x7pSRmYkdRkwQmHFptJGphakxKCywU/CR5zIz88nac9KXlr3Jp+78rZYlxN1KUnJhGtMtLa2SrsPIURUXDMpm40ljUzJT8Zhk6/D/iTdCeKE3W7n9sR0/obB/kP7Y11O1DkcDsKaiYaGhliXIoQYpBKtZi4YkcbuqiYO17XEupwhRcJHHLn+wquZ0VLH13auYPf+3bEuJ6qysrIY3lbD17a+waOv/oGVH73baRh+IYToK+cVpKBrGusP18sIzP1EwkccMZvN/PBjX2BSWytf2reeF95ZQjgcjnVZUWE2m/n5vM+xwJLAjlYvn2xxsWL9e7EuSwgxSOV7EhmX7eb9fXW0+EOxLmfQk/ARZ+x2Oz+67h7u1C08FWhi4X9+w6GSQ7EuKyqSkpKYkjmayqR07mx8l4vP63p+ICGE6AsJVhNzR0Yuwxyqlcsw0SThIw6ZTCbuuOIW/jL+IsxKcefO1fxoyf92DA42GCilePm9V/l21T7mB/386JovdzlfjBBC9LXzClKwmHXWHaqPdSmDlozzEefC4TAr1q/iz5VbKbWnMq61lhuyxnPBpPNJTEyMdXk9EgwG+dXrf+Elm5MvmkzcdumNMtS6EKLfNftD7K70Ma3QE+tS4kJ3vr8lfAwS4XCYTds388LB1Wy0OgCYGWzj02MvY+TwkXHz5V1eXs4jH73APpuLB7PHMXvKzFiXJIQYwiq97fjag4zKlC7/ZyPhY4hrbm5m1ZbVvFC9k52OHC5rquRHN34l1mWdIhwO4/V6aWxs5FBNKe9U72CV1c3I9lp+MP1G8nLzzr4RIYSIsj1VTbjsFrLccun3TGSE0yEuKSmJBXOu4PLgJdz7r5/RmND3U0Ufy6xnGno4FAp1hIuK+kr2N5VT1tZApRGgDp16cwJB3QKAPRygwAjx/1Jyufi8T2A2y6EphBgYRmU6WXeoHqfdLIOR9RE58zGIPbnkD/w6aRzTmjdh1UGhnTD7WyQ0dPxbA+Po43Bs3aPraCc+99gyDWs4yM/HX0FxcXGn133no5X8rnoHDaYEQnrkD9ViBEkJtZGmDDJNVvLtyQxz5ZLjycLtdpOSkiLDqAshBiylFO/vq+OCEaky38tpyJkPgWEoCl3j+H7rAexJ6Wjo6Bro6IAW+bemo2saGhq6pqMdfVzTNHTt6DJA10wnrKehaTr+Nj/P1G3lrj2ruXvTRyRnpLCvqZzDgWbW2tOZ1dbGvcVTyPZkkpycjMvlkrMZQoi4pWka5xUmy3wvfUTOfAxSW8oaGZ3lxGaO3tmEYDDIjW/+mUaLm5Sgj/RwO1mazrysCVwy/UL5dSCEGHSONLZxpKGNsdlOnHZLrMsZUOTMhyBsqKgGDwCLxcKzc25G13WcTmfc9KgRQoieyk1OIDc5gQ8P1DE5L5kEq1wu7gn5thC9kpKSgtvtluAhhBhSZhV72FTaGOsy4pZ8YwxSOckJMkujEEJEiaZpDE938MG+WkJhI9blxB0JH4NUpstOQ2uQumaZCVYIIaIhw2Xn/GGpfLC/DsMYUM0nBzwJH4PYlPxkKn3t7KlqinUpQggxKOm6xpzhqby/v5YB1n9jQJPwMciNz3HjsJnZVy0BRAghosFs0plZ7OGD/XXUtwRiXU5ckPAxBOQmJ2DSdXaU+2JdihBCDEo2s4kLRqTx7p4aAiFpA3I2Ej6GiOI0B1luO6v31+FtC8a6HCGEGJQm5bkpa2iNdRkDnoSPIcTjsDJ7eCoV3jbWHqqnpkkaowohRF8alp6ErmnsrpRL3Wci4WMIGpPlYkaRhxZ/iA8P1MW6HCGEGFSK0hwk2c1sO+KNdSkDloxwOoQVpTlIc9pYtbeWLLfthDldODqHC0fvR5ZpJz124v3O63deRwghhprc5AQsJo0NJQ2cJ3PBnELCxxCXZDMza5gHf8jAUAplgKHU0VtkJkdDQchQGMpAKToeM5RCnbDOseeduM7JwkZkGxPz3FEf/l0IIWIpw2nHatL5YH8t5xenouvyY+wYCR8Ci0nHYuq/K3CGodh6xEswbDAm20WSTQ5DIcTglJxoZUaRhzUH6xmVmURqki3WJQ0I8qkv+p2ua0zOT0Ypxa7KJloDIdKT7BSkJsa6NCGE6HMWk87s4alsL/fS4g/LZx3S4FTEkKZpjM12Ma3Qg92i89HBeg7Wynw0QojBaXyOm/ZQmAM1zbEuJeYkfIgBIcNlZ2axB4fNxLpDEkKEEIPTqEwnVrPOlrLGWJcSUxI+xICS4bQzvcjD3qomGSVQCDEo5aUkUuiJzIg7VCekkzYfYkCaNzaTjw7VY7eYUEqR7U4gy22PdVlCCNEn3IkWZhR7+PBgHQ7r8a/ik6NIqsNKvmfwtRHR1ACbhs/n8+F2u/F6vbhcrliXIwaI0vpWDta2MLUgGafdEutyhBCiX5TUtdLYFmBSXnKsSzmr7nx/y2UXERfyPYlcODKNkvpWGTVQCDFkFKQmUuBJ5IP9tbQGQrEup89I+BBxQ9M0xue4yU9J5IN9tbQHw7EuSQghoi450crsYansrPBR7WuPdTl9QsKHiDvuRAuzh6eyqbQRb6vM0CuEGPw0TWNaoYfGtiAbSxpiXU6vSfgQcUnTNM4flkpJfSt7qmT2SCHE0DAq04k7wUJdc3zPSi7hQ8S1iXluPA4rH+yrJRiWrrlCiMGvOM3BzoqmuP7Mk/Ah4l5ako3zh6Wyen8d4SHaZ14IMXRomsac4amsOVDPAOuwes4kfIhBQdcjf4yvbq2g2T94WoQLIURXdF1jWmEKH+yvi8vG9xI+xKBhNulcOzmHLaWNcfnHKIQQ3ZFgNTFneCpbyrxUxVkvmG6Fj0WLFjFjxgycTicZGRlcf/317N69u9M6mqZ1eXviiSf6tHAhTmf28FQ+OlgvAUQIMehpmsbMYg81Tf64CiDdCh8rV65k4cKFfPjhhyxbtoxQKMT8+fNpaTk+CVhFRUWn25/+9Cc0TeOmm27q8+KF6IqmaVw4Mo3V++vYUe6LdTlCCBF1E3LdcRVAejW8ek1NDRkZGaxcuZKLLrqoy3Wuv/56mpqaeOutt85pmzK8uugrobDBhpJG0p02itMcsS5HCCGibnu5l+REK7nJCf3+2v02vLrXGxnm2uPxdPl4VVUVS5cu5XOf+9xpt+H3+/H5fJ1uQvQFs0lnZrGHRKuJNQfqpCGqEGLQG5/jptLbTltgYF927nH4UEpx3333MXfuXCZMmNDlOn/5y19wOp3ceOONp93OokWLcLvdHbf8/PyeliRElzJddmYNS2V/dTMHa1vO/gQhhIhj5xUkD/hRUHt82WXhwoUsXbqUVatWkZeX1+U6Y8aM4YorruBXv/rVabfj9/vx+4+P1Obz+cjPz5fLLiIqSutbqWn2c15BSqxLEUKIqPG2BiltaGVCrrvfXrM7l13MPXmBe++9lyVLlvDuu++eNni899577N69m7///e9n3JbNZsNms/WkDCG6Ld+TiMdhZdXeWqYXpWC3mGJdkhBC9Dl3ooWqJp2aJj/pzoH3Hdut8KGU4t577+XFF19kxYoVFBcXn3bdP/7xj0ybNo3Jkyf3ukgxNH10sJ7DdS2MzHT2+bYdNhPby31MK5QzIEKIwWlUppPV++tIdVjRdS3W5XTSrfCxcOFCnnvuOV5++WWcTieVlZUAuN1uEhKOt6z1+Xz885//5Cc/+UnfViuGlJnFHorTHOytbmJKfjKJ1h6dqBNCiCFrelEK6w43MLO4644hsdKtBqe//e1v8Xq9XHLJJWRnZ3fcTr60snjxYpRS3HbbbX1arBh60p02Zg9LZU9VM7sqpSeUEEJ0h8WkU+BJZH9Nc6xL6aRX43xEg4zzIU6nviXA7somJuS6cNotsS5HCCHixtYyL4Vpibii+NnZb+N8CNGfPA4rs4encqi2le3l3liXI4QQcWNinntAjfgs4UPEnYl5bvKSE1m9v46GlkCsyxFCiLgwLN3B3qqmWJcBSPgQccqdaGH28FRqmv18dLBeRi8VQoizyHDaafaHBsTopxI+RFwblelkRlEKh+taWHtIZrIVQogzmZI/MEY/lb6LIu5pmsb4HDdKKbaUeQkZBpPzkjGbJFsLIcSJNE0j0WZGKYWmxW7sDwkfYtDQNI3J+cmEDcWm0kYcNhNjsqTHlBBCnMiIcfAAuewiBiGTrjGtMAWPw8qaA3UYxoDqTS6EEDEVCBmxLkHChxi8Mpx2phaksLG0MdalCCHEgDEQ5rSS8CEGNatZxzTA5jQQQohY8g+AhvkSPsSgNsAG8BVCiJgbCD/IJHyIQU8CiBBCRAyUz0MJH2JQ0zSNYHhg/LEJIUSs+dpDeBzWWJch4UMMfsPTHWyWRqdCCIE/FMZqjv1Xf+wrECLKUpNs5KYksOZA3YA55SiEELGgaxoD4WNQwocYEtKSbEwpSGb1gToqvG2xLkcIIWLCatbxyzgfQvQfm9nEnOFphMKKtYfqY12OEEL0u8O1rRSlJsa6DAkfYujJ9yQyMiOJrWXeWJcihBD9KmgYA2Leq9hXIEQMJCdacdrN7K1qinUpQgjRbwZKuzcJH2LIKkpz4LRbWHOgjmA49tdAhRAimjaVNjIq0xnrMgAJH2KIy3LbmV7kYfX+Ovyh2A85LIQQ0RAMGyilcNotsS4FkPAhBCZd48KRaWwqaYx1KUIIERW7K5sYm+2KdRkdJHwIQWQk1JzkBMoaWmNdihBC9Dl/KDwgZrM9RsKHEEflexKpaw5Q1+yPdSlCCNFnqnztZDjtsS6jEwkfQpxgcn4yRxrbKK2XMyBCiMGhrKGNfE/sx/Y4kYQPIU4yKS+ZsKH46GC99IIRQsQ9TYt1Bacyx7oAIQaiojQH+Z5ENpc1ohRMznMPiIF5hBCiOyq8bWS5BtYlF5DwIcRpmXSN8wpSCIUNth7xYihIdVgpSnPEujQhhDgnFd52zitIiXUZp5DwIcRZmE06U4/+8dY1+9lY0oCmaSilyEtJJN1pi3GFQgjRNX0gXnNBwocQ3ZKaZCM1KRI2lFKUNbSx/nADAC67mREZSWgD9I9dCCEGCgkfQvSQpmnkexI7WpH72oNsKm3seEzXoDDVgTthYIwoKIQYWkJhA2OAzOVyMgkfQvQRl93ScXkGIGwoDtW1cLC25ZR1kxMs5CQnYDX3vBGrYShqW/zUNgUYm+2UMy5CiE62HPEyKdcd6zK6JOFDiCgx6RrD05O6fKyxNcDe6iZCYYUCNODE3ydKqU5h4uT7ALoWuQxUmJrI+/vqmDsyre/fhBAirg3UXnoSPoSIgeREK8mJ1j7b3uR8N1vLvEzMG5i/coQQ/UsN0MstxwzMSCSE6Ban3YKmQbM/FOtShBADwPrDDYzPGTgTyZ1MwocQg8T4HBe7KnyxLkMIEWNVvnbSkmzYzANnIrmTSfgQYpDQNA2TLo1OhRjqyhpaB/xgiBI+hBhEjg1+JoQYmgxDEQ8fARI+hBhECj2JXXbtFUIMDdvK46PhuYQPIQaRFIeVSl+7nP0QYogKho0B3dbjGAkfQgwyk/KS2V3VFOsyhBD9TCmFSY+Pr/X4qFIIcc6SbGZaA+FYlyGE6Gcl9a3kpyTEuoxzIuFDCCGEGASqm/wdE18OdN0KH4sWLWLGjBk4nU4yMjK4/vrr2b179ynr7dy5k49//OO43W6cTifnn38+JSUlfVa0EOLM0hw2KrxtsS5DCNFPDEPFVVf7boWPlStXsnDhQj788EOWLVtGKBRi/vz5tLQcb12/f/9+5s6dy5gxY1ixYgWbN2/mgQcewG6393nxQoiuFaQmUt7YRmNrINalCCH6wYHaZkZkdD2X1ECkqV40i6+pqSEjI4OVK1dy0UUXAXDrrbdisVj4v//7vx5t0+fz4Xa78Xq9uFwDd2hYIeLBtiNeHDYzxQN8wCEhRO9sKm1kSn5yTGvozvd3r9p8eL1eADweDwCGYbB06VJGjRrFlVdeSUZGBrNmzeKll1467Tb8fj8+n6/TTQjRNybkurGYNLaUNca6FCFEFMVb9/oehw+lFPfddx9z585lwoQJAFRXV9Pc3Myjjz7KggULePPNN7nhhhu48cYbWblyZZfbWbRoEW63u+OWn5/f05KEEF3IS0mkwJPI6v11cfcBJYQ4ztsWpMLbRllDKyV1rRyqbWF/TTN7q5oIGfH1t93jyy4LFy5k6dKlrFq1iry8PADKy8vJzc3ltttu47nnnutY9+Mf/zgOh4Pnn3/+lO34/X78fn/HfZ/PR35+vlx2EaKP+UNhPjpYz6ziVKxm6egmRLxZtbeWkZlJ6EfncdI10HUNk6Zht5hi3uC0O5ddzD15gXvvvZclS5bw7rvvdgQPgLS0NMxmM+PGjeu0/tixY1m1alWX27LZbNhs8dE1SIh4ZjObmDsijQ0lDUwr9MS6HCFENyXZzWS6BkfnjW6FD6UU9957Ly+++CIrVqyguLi40+NWq5UZM2ac0v12z549FBYW9r5aIUSvRCaei/wta1r8dMsTYqhrbA2QZOvR+YIBqVvvZOHChTz33HO8/PLLOJ1OKisrAXC73SQkREZV+9a3vsUnP/lJLrroIi699FJef/11/vOf/7BixYo+L14I0X0T89ysPlDHnOFpsS5FCHGODtW1MjkOJow7V91q83G6X0rPPPMMd955Z8f9P/3pTyxatIiysjJGjx7ND3/4Q6677rpzeg3paitE9DW1B9lc6mV6UQp2y8CfhEqIoe7tXVVMK/DgTrTEupTT6s73d6/G+YgGCR9C9A+lFFuPeEm0muNqcCIhhqKBMI7H2fTbOB9CiPilaRqT8pIx6Ro7ymV8HSFE/5HwIcQQV5zmIMNl48MDddQ2+8/+BCFEvxtgFyl6bfA0nRVC9Fhako20JBv7a5o5WNOM442vEziyHS1tBHDyB1/k3+rooEbqpPvasftKnfLcjn93PNZ5mx3Ljy3tGDjphNdXp9l+F6930osc38ppt3Hyc06s/czb0E6q/Uyve/L7PHl91MmPnbpQdbnisUWnFHvyE7v+Mjv6OsqI/PeCLz+Ac+rNp64n+l28DSJ2NhI+hBAdhqcnMdxjZ+m7HzBsg5WDwzajZZtBM3VucH5y2/Ojj2knP95FI/WO7XR6SOu4r53w7xPX0zj59U98zkkv3ukltM4rnPzyJ9auHYtO2vHST3gP2rGbpkWWn/z+9BP3g9b54VP2kdb5vye+x8iL0GlrJ73nTu9QP2nbndbTulh0bJsnPKOjHh1N16hctp2qLcslfAwA28u9jMlyxrqMPiXhQwjRmcnM1X/bR8O7T7H/p79EVxrzH3gac9GsWFcm+tHSDePOvpLoF62BME77wO3l0hMSPoQQp9I0Ui5eyMfGXsFbD3+SZV+8naLrpzP200+CKzvW1QkxpLjsFjaWNBA2FCZd63TGKhg2KE5zkJYUXyOFS/gQQpyWnjGKK362liNLHmTj317gwKuXkHnRaKbe+A1MxReCLm3WhYi20ae55LKvupn6lgCpDms/V9R7Ej6EEGdmMpN7w4/JverblP7nYba8spRly75AKF0nc/JIJs/7JNZRV0BSRqwrFWLIeG9vDeNz3HE7Ro+EDyHEubG7yb/lCfJvegzj8AfseO1/Obx+M8vf+R/gfwin6DjyPOSMGkvuiMk488eDuwASPWBzgekcP24MA/xeaKnFqDtA4+GNVB3aRf64WSSNmQepI7psyCrEUHGksY2iVAeeODzjcYyEDyFE9+g6evFcJtwzlwkA3iMED73PwfXLKdmxlb3vvM/+l99FCx0PCMqkUDYNZdU75YZjPUg7FoUVtBvowaO9MRSE7UCSzoGXV6OHfk4oWcNRmErOqLEMm3wx1rzJkD4azPF1zVuInqr0tsX9zNQSPoQQvePOxTL5E4ya/AlGHVsWDoLvCDSWQlsDbY1VNNUdoamx9vgYFEdpaGi6hoaOxWrHnZ6HIzUXPSkdUorAlQO6Cdq9GCVrOLjmZQ5u3sDed97nwL/fQwsDBRau+vVqsA2u7ogxNbiGlRg02oNhTu3rHn8kfAgh+p7JEgkOKUUAJBy99apViN2NPmo+w0fNZ/hnji5r98KhVbzynftY+8QNzPjuq2CO31PRA0/8f8kNNtvLvZxXkBLrMnpNmqoLIeKX3Q1jrubShx6menUpb3x1BuHDa2JdlRBRY9L1084wH08kfAgh4p5j6i0s+N3zKEOx7O7bWXn/XII7/hO5/CPEIBIMG7EuoU9I+BBCDAqmvPNY8NRG5v7P/bTW+3jr69/kP5+ayJpHrqJl9R+gsaTz/ClCxCGLaXB8bUubDyHE4KGbSDr/Tq6adQdUbWPXKz/nwEfrWPnwE2jhJwg7wZyZRPbIYUz81CPo6SNjXbEQ3RIaJGc+JHwIIQYfTYOsiYz5/B8Z83mgtZ7wwVUc2baSkh2bKVmzjbLl1zDxM9dQcMPDYEmIdcViiGtoCbCrsokEq6lTM18FOO1mQmFFU3sw7oZRPx0JH0KIwS/Rg2n8xykY/3EKAPzN7P6/r7Ltz6+wZcmrzLztNjIu/zrYXTEuVAxF1b52Dte3Mnt46imPKaVo8ocIhIzTDrMejwbHxSMhhOgOWxKjP/8nFvz5P6SPK2TtU3/jlU/NoPTFH8S6sgFEyUiy/cDbGqSkvpUZRV0PGqZpGi67ZdCc8ThGznwIIYYsPWMUs773OvjK2f3cN9n+1N9JSs0l5aIvxrq0gUHCR9RVN7WT5bbHuox+J2c+hBDClcPoL/6NnI+NZfWjPyWw951YVySGgF2VPuwWE3kpibEupd9J+BBCCABNY9K9/8AxycOyb3yJ1o0vxLoiMYhtL/fitFvI9wy94AESPoQQ4jizlYv/ZzmeaTm8++3vs/aRBVC7N9ZViUFmX3UzLruF3OSh28tK2nwIIcSJrA5m/+BtGlb+hvef/g2v/dc12CenMeuWL5E45RMye67oNm9rkNZgiJomP8GwQinFmOyh3bNKznwIIcTJNI2US77CNX/ZxPnf+TKBlnbevf9h1jx2XawrE3GmPRjmpU1HUArGZbuYVpjC9CIPSbah/dtfwocQQpyO2UbKpV/nyl+vZ/b/+yqN7xyked3iWFfVb/Yse4fNP78p1mXEJ38T/tJNvLdlH7fOzCcnOQHzIBkavS9oSg2syQ58Ph9utxuv14vLNbRPSwkhBhCleOcb02ltaOPqZ3bEupqoK3v5QQ68txTf/iaueXF3rMsZmJSCpkqo3UPDgY8o2bGWukOHaavyoTUaaCEwrJB7yRjsrlRam7y0NTfjb2kh2OYn3B7A8IfAFyB91mhmffeVWL+jXunO9/fQPu8jhBDnKHT4I9q3NzPhsx+LdSn9Iu+6H5Jgs7N6/59jXUrshQLQcJBw1Q4qdq7myJ6tNJZVEqxpxdQcWSVsU5jSbDhz0ii4Ygr54+ZgyxrDkTUvsPHlV0DX0KxmTHYLlgQ7iSluEhx2Aoc3E9zjwGazxvY99jMJH0IIcTbBNt780eewjHJQeMvjsa6m32i6HplcZKhqquLd719Cy57IZG5KA8MJ1gwHnsJccudPImvMBegZYyC5EEynfqXmFswi95YnOi9s91H91i/46O/Po9ckUnTHdMZ9/o/98Y4GDAkfQghxJobBhp/dhKoKcvkfXwCTJdYV9RtN0xmy6aNqB3v+8X3atymGXzuMUTd8H9JGgSO95yO/hkNUv/EYa/70LHobpF1QzMzHf4KeNb5va48DEj6EEOIMSl64n+rl+7noR99DzxwT63L6l8aQzB51bz7B2if/QChFY+znFjD80z/v/UYbDvP2wzcT2Oqj8GMTmPTZX4I7t/fbjVMSPoQQ4nTCIbb88yWG3zQT56w7Yl1Nv9M0E3pAw2gsQ0/Oi3U5fSMcgqZyqN1D3dq/k2BPIPHSb+E/9CHv/elR2g83YWnWCWfpXPOXzWDuZVuMQCvlS3/Mhr+8gOYyccVv/hfryEv75r3EMQkfQghxOs1VmJpg7CWf7li0aNEivve97/G1r32Nn//85wA89NBDLF68mNLSUqxWK9OmTePHP/4xs2bNOu2mn376aZ599lm2bdsGwLRp03jkkUeYOXNmVN9SdyTmTiClRufAfxYx4jO/iXU5Z6cUeMugYhOkFEHWRBre/xO731qM3ZFIfUkJrYeaSSszETQr0ryRyyflGW+CBsFhigu+cjcmi4WEvKm9Cx6GQc2bT7DmL8+g+RSFV01l4ud/B3Z3n7zVeCfhQwghTseRTjgJdq18njGjF7B27Vp+//vfM2nSpE6rjRo1il//+tcMGzaMtrY2fvaznzF//nz27dtHenp6l5tesWIFt912G3PmzMFut/P4448zf/58tm/fTm7uwDgdbx0zn/rRGumGEZsCjDC01kW6syoDsibif/0B3l68GLPFIBTUCbeZIKBhTwvgr7KiBzU0AwpLdUrntJGxNgF3EPaPC+EamUnxFaMYe8F16O4cyBwPJasZiwY2J2RNhMSup7bvVtk1e3nvp3fQtr6O7HkjmXb3ryNhSHSQ8CGEEKdjtjL5thvY/rsXSR71Bz79hcd5+umnefjhhzut9qlPfarT/Z/+9Kf88Y9/ZMuWLVx++eVdbvpvf/tbp/tPP/00L7zwAm+99Ra33357376P3lAK9C4Gx9ryT6g/AEUXQPYUsCX16mWMim0c+Pu3OLCnErNVo62uHeUNYQpomP0aeRUah2f5se234gzaSPUef275ha2ovQkMq9QY9ef/xjTycqr//U0q/rITWxDKshTnfeYWcm545NQXHnttr+ruJNjGgcXfZOfi5ahUE1f87KfYxl/dd9sfRCR8CCHEGeTf8AhVezbxX//1Da689GLmzZt3Svg4USAQ4Pe//z1ut5vJkyef8+u0trYSDAbxeHr/y7svKQV7/7qcXUtGozssFM+azNiLbmHnJ34AQEXaM/iLA1z17ccjv+7tbggHwJVLaN87mF2Z1LzzFK5RF2I7/+5IkGmppfrtX7Hr/eU0lVSigjrp+824WiH/hNfeP1ZRdMjUcd+e5uGCr34dy8jLaH79IUrLKxh77X2MHX4pGAbU7YX00QBk3P0il31eQc0uxqaO7LIbbJ9qqeWN/76Q0BGD8Z++kqKbHwXr0Jyx9lxI+BBCiDPRdfYVfJqDwVXcW7GTshe+HflGPskrr7zCrbfeSmtrK9nZ2Sxbtoy0tLRzfpnvfve75ObmMm/evL6svtfmP/gLvAc+Ihhoo+bwXva/uZ7DL64jNMYgs6CV7Ddd1IasvPrt72Nr0cip1jhYbGBpj5ytOKaW9ZTOfpRRk4az7b3DWBtMmMcaDNtwarsKy60mXCNnM3bBd2lZ+XPMaaOwXfBlxp4QIJI+8XvGnvgkXe8IHh00DTLGEnX+Zlb9z5WEywyuevrv6LlTov+acU7ChxBCnEFpaSlfu++bvPn6uzi2/J6tf3iZ+r1lMG5kp/UuvfRSNm3aRG1tLU8//TSf+MQnWLNmDRkZGWd9jccff5znn3+eFStWYLfbo/VWesQ6Zj7pY+YDkANM/nxtpA1Gxljatr7E+jX/j2mL7iZx+Fx2zr8TgAVfv4V3X/0PBxztDNt3/JKNyWxQ+vcyzC6d2U88iHP6bRz+65dpfXhFxzrt1/gZ+9CBjvuOG3/VH2+zZ5Si+aO/sOKXj0GTwfxHfizB4xzJ3C5CCHEGL730EjfccAMm0/HT/+FwGE0DXTfh9/s7PXbMyJEjueuuu7j//vvPuP0nn3yShx9+mOXLlzN9+vQ+rz+qlIKWGkjKgEALDa//CPe4+egjLos8HgpA3b5Ie5AED1gdULUdTFZIHxVZx1dOcOtLWHInRwbxSuq6ge5AY1TuYOVP7sK/qQHXBTnM+dozQ75RqcztIoQQfeTyyy9n69atnZbd/omPk9JYy713nI9JhYBTw4dSCr/ff8ZtP/HEEzz88MO88cYb8Rc8IHJZI+nomR2rg5SPP9r5cbMVMsd1XpY1ofN9Vw6WC+6JXo19rWY3G/7vu1Qu24HKNjPvZ09im9CHjVaHCAkfQghxBk6nkwkTOn9hujIKGDE2H+vmw7x49zQ+NI/jhrv+m+ycHOrq6njqqacoKyvjlltu6XjO7bffTm5uLosWLQIil1oeeOABnnvuOYqKiqisrAQgKSmJpKTe9RwRfSjkhyMbOLLuRXa+v5Lg9kbCqTrnfeEWcq59AMy2WFcYl7roP3V6ixYtYsaMGTidTjIyMrj++uvZvbvzVMt33nknmqZ1up1//vl9WrQQQsRaQv5Urvz98yQ4bax6+TWuueIiRo0cwTVXX01NTQ3vvfce48cfn7OjpKSEioqKjvtPPfUUgUCAm2++mezs7I7bk08+GYu3I06y/fef5ZXPTuCVmybx2hfuZONzL4Ouc8H3v841z24m54aHJXj0QrfafCxYsIBbb72VGTNmEAqF+P73v8/WrVvZsWMHDocDiISPqqoqnnnmmY7nWa3Wc+4+Jm0+hBBxRSkC2/7D+399lLYtdSgz2Ed7mHL5taROuhKyJg2pyegGhYZDLP3MlWSMMzN83u14JlwJmROi3103znXn+7tXDU5ramrIyMhg5cqVXHTRRUAkfDQ2NvLSSy/1aJsSPoQQcauxlLoP/8qG11/Ef9CLqR3CCeAcl8n5N9+DbeL1YBlYvVnEUSE/Ruk6ti/9NSVvbkBLNfOxX74FzqxYVxY3+q3BqdcbGWLu5LMaK1asICMjg+TkZC6++GJ+/OMfn7a7md/v79Qoy+fz9aYkIYSIDSMMiamkXvFNrrjyu5GBtiq2ULVxCRtff4W373+QsONBUqcWMvP2/8FUKJejY82oO8jqX32expJqVK0fk18jlKIx8ZPzKbrpx2CXH8DR0uMzH0oprrvuOhoaGnjvvfc6lv/9738nKSmJwsJCDh48yAMPPEAoFGL9+vXYbKdeH3vooYf44Q9/eMpyOfMhhOgxIwyBZvA3nXDzYbR5aW+qobmhmpbGWlqbGmj1+UAZWO0JWO0JWBKTSHQmk+hMxZGcgSXRTTjQiq+mBG/NEbw1FTRWV9Fa7yPsa0f5w2hB0EIKLayBDkpXKJOGsmroDjNmZwLOBANTWxX1DVa0SguWCS4u/tqvMRedfvI5EUX+Zjb+/Gaqlh8k82MTKZ40h+Sxl0Xmd9FP7b0kzq5fLrssXLiQpUuXsmrVKvLyTj/VckVFBYWFhSxevJgbb7zxlMe7OvORn58v4UMIcXbhINTuJXBkE4c2reDInp20HKlDawyhhbQun6JMCsOqodl0sJrQbSbQNVQwjAoaqKABQQMtqNCDGhz9hDSsCpVgQk80YXU7cKalkpKVgys1C7vDjT0pBVuCk1CwjUBbM4H2FpobqmmoKsNbW0trvZdgbQsmn4EejEx8drjAYMF/1kUmNRPRFQ6Br4xg2Qa2vPEs5R/uRPfDnG9/g5QLvxDr6gaFqF92uffee1myZAnvvvvuGYMHQHZ2NoWFhezdu7fLx202W5dnRIQQopPWeowjG6ja9g4HtnxEY0klqjaAyQ9KByNZx57tZtjcGRSMmU6iJwfd7o58sZ94syadW8NBpSKXTvzNkQajNmdkXIuzsB69AXiAgpNXaGuAmt3UbluOpboULI7u7QdxVuHSdRx850/UlBzEV1VNsKENzRsJpJoCI9vC6KvmMurmH4L7zN9hIjq6FT6UUtx77728+OKLrFixguLi4rM+p66ujtLSUrKzs3tcpBBiiDEMqNpG447l7PpwGXV7S9Gqg2hhCDnAkuUga9xIhk2aTcrIOZH5O/pgKvRONC3SlbKvu1MmpEDB+aQVnM+5z/wizqqtgcCuN1n78u/xrS4jlKJh9iTizEwlY2oR2cMm4cwfD6kjIPmUSCj6WbfCx8KFC3nuued4+eWXcTqdHYPiuN1uEhISaG5u5qGHHuKmm24iOzubQ4cO8b3vfY+0tDRuuOGGqLwBIUScCwWg4RD4jtBcvpMt77xE3Y4SzA0Kw6rQchPIO288o86/moRhc8AzrOsp3sXQEWwjuHc5u99ZTG15Gc2lNei1YTQDVIGdmd/4PGmXf126OA9g3WrzoZ3mlOMzzzzDnXfeSVtbG9dffz0bN26ksbGR7OxsLr30Un70ox+Rn5/f5XNPJl1thRjElILavTRsWcL2917He+zSSUBDaaBMoOXaGXX++Qy76FPo+TNkWnIR0e6l8YNnWLvkuePdmDNMWFOd5I4ezYjpV2AbPhc8Zz8jL6Ijam0+zpZTEhISeOONN7qzSSHEYNZaD7V7qNv+Dts/eBPfvgpM9ZEzGqb8JHInjaV40hzcw2aAKxec2TIOhjiutZ7mjf9k/at/o2lbFXoI7OM8nP+l20mbdiOkjzmndjhi4JHh2oQQvacU1B8gcHgNe9e8RtnOXQSqmzB5FZqCsA0sBUmMnn8xw2bfgKl4bmSmUyFO1lhK3eq/suGNFwns9aIpMA1zMP2uW8i69B5w5cS6QtEHJHwIIbov2AbtPgIHVrHlzWep2LQHc50R6XWSasJZlMmoWbPJGz8HS/bESCM/mQdDnCjYDhWbqd/1DiU7N1BTUoK/2oepJoxhhYTRHi74xpdInnGbjDI6CEn4EEJ0TSloOEi4fDNHtq+idPdWvGVVhBvaMbUAiki3xRwLwy+cwZhLPoWePx0c0odDnEQpaDhE4PAa9q15jZKtWwmVtUTabdhB81hIzEwh78KZFE2dR8LEa8HujnXVIookfAghIl8OjYcJlqxh/9o3Kd22jfYjjZh9R8fQSAJzhoP04YXkjBhLRtFELI4UyBgnDfzEqZTCKF3Hgbf/yKGtm2krbcDsUygtcmYseUQOY6+9hdRJC45O2Ca9UoYaCR9CDGZKRQa1amuAQEvHzWj3EWxpoLG6hJ0fvkPTvmrMXoXSIezRcRamM/qma8ibeiV6ziRIypSGfeK4QAtU7wQjBLoFlIHhLaPu8DZKdm2gYvNuzBVhgqkajsI0Jl2/gPzzFqDnTYOk9FhXLwYACR9CREs4BL4j0FiC0VxNa2MVzQ2VNDfWEmxrwTAMjHAIIxwGTcOW4MCe5CLB4SbBlYIjORtTcl7kercz+/RdToNtULWd1tKNlGz/kMoD+2ir9xJu8kOLge7v+mnKBMoK1nwX4z9+BUXTr0bPOw+Sup4EUgxB4RCh/SvZsvQp/C2tNNfV469vQasLoIci3aNPZNhBS7GQP2Mi4+d9FvPoK2SeFNElCR9C9IVAK8bhDyjb8BoHNq+luawWGkORuUGOUmaFsmpg08Gsg6ah6XScUYjMLaLQggYcm1fkGB3CiWBKtuPKTSd3xFh89TWU79hFuLwNUwAME6gUE7Z0J56CXFKyckjLG05y9ij0pLTIsOLWJLA6jt/kdLc4WbsPo2QN61/8JZVr9mBphlC2GZPDRkKKk6zhwyiefCFJw2eDOQGMIKBFAnKiR86QiXMi4UOIngoFaN30Tz78x29o3V2HqR1CLrDnJjPsghnkjpyMO38SpBSCIz0yN8i5/gpUCvw+aKqCpgoCdSXUlGynfP9O6kqOsG3XMjSbTurIAsbcfDkp4+ZB2mgZkEucG8OAur3UrnuBLStew9/USrg9iGoLoTdFJr4LZ5qYcuvV5M++DfKmS6gQfUrChxDd1VjCvpcXsev1dzDVK0yjk5j1hf8ifcbNkQDQF0N/a1qktb/dDemjsA6D3BmQ2/sti6EmFICqbdRvX8bede9SX1pOuKYVU1Okp4ltWDIpuVkkOJ04UzPIKBhH0th5ke7REjhElEj4EOJc1R9g/R+/TtU7uzESoPDSqUy85QeQOS7WlQkB7V6o2UO4/iA1h7ZSdXgPFbv2EipvwdQWCRp6lp2U/GwK5k0me9J89MLzI5ffhOhnEj6EOJtgO4de+A7b//o6yqUxc+F/kXHFffKhLfpO3X6o2wdpoyCl6NzPOBhhjLL1bHnxccpWbEVvP7o4EXBaSC7KYsy1N5E2YT5kTZSh68WAIeFDiDPxlrHsgWsJ7m1l7KfmMezWJ8GSEOuqRDw52r6i7cBqGir2EmxrIxhoo8VbT/Whw7RXeTHXGpF1NQg7QXPbsDoTSHA5MdusWGx2MAz87W0E2v2E/AECvhaMunbMTRBK1xl/0zyKL/9CZLp4e7JcMhEDmoQPIU7DqDvIq1/7GGhw1e//hil/eqxLEgPd0TluSt76DTveXUmoqR2aQphaIl2bjURQZg1N18BmwpHpYeSlFzBsxlVYC6ZjVO+ibNOb1Bw5RFNdHS0NXoxQGCMYRtNAt5oxWS1Y7FbcwwvIXTCerKnXohfMApN8nIv4IUerEF0JBVj2/65DM+tc/ctlkJQV+QV7rvqi0akYGJSKtKdoquzoedRYdRBfYw1t3npafF7am1torfcSrm3D3AThBHBPysEzYRzp+SNJG3VBZOj5swwZrqcUUjD6Sgr66a0JESsSPoToQuvGxaSuDlKVZ7D0U5d067mZZSay/9/Hyb35segUJ06lVLfaSVC3PxIojNDRW5BQUw11pTupPbKfhsoKWuoaCXpboTmM3nbC8/XIYFrKpqPZTJjsFswJNlILcsm7YgJZYy/ENPwSmbVXiDOQ8CFEFxInXs/4/w0xzgh263m7//0o5Rqcd9FdUapMnChcs5ctf/kGR9bvQfkV2HU0s0bKiDwyi0eS5E7DlpCEt7acmrID1B0sJVQR6f1x8uicEDljoTnNmF2JJKWlkDphPKl5I0jNG40ppSAy2mxSljTcFKKXJHwI0RW7i4Tzux8gHDvXYhxahZ6Q3Pc1iVOUvfVbAs/tJedKE3lzbqGpsZZWbx1lO3bRsKsU/AaEFCpBx+S04cpNZ+SCa8iccHlk4DfdHBn4TTdDQjLYXNJQU4h+oCmlVKyLOJHP58PtduP1enG5XLEuR4ju8R7hlYXzUCE4//ZPkz73c+DK7v52lIKaXRx59xk2L3+T4qmTGPvpn8ikXCcLh/jgh5fRcLiOq/+yPdbVCDGkdef7W8KHEH3NV87a33yO6vcPoIXBSDdhS3Piyc3BnZoBmg66jkak14OGTjDQhr+1mfaWFny1NbSWNWCuMwgngnN0Kk07awH42KM/Qx/zsZj+Ojf2vkXj4rtxXPLfNDVU4plzF3ra8P4vxN/EkVcfZ8PiF0jIdjDv5+v6vwYhRAcJH0IMBK31tG5bwqGN71B9+BAtVfWo9hAoUHD0/0X+q5k1sOjoVjM2dyL5YyYwfM6NkYaLFjtGyUe89uXPYApAKFPHM7qA3BHjSXSnkujykOBKR09MBtvRIdlTR0Stx83BRTNo/0sz5RmKnGqNrF9/jpR534zKawGd57nxllC39yO2r16Gb1s5mh+Sp+dywX3PgjsvejUIIc5KwocQg5ERJrx3OduX/ZkjO3YTqmtFC6rILRQ5E6IZUHRYJ/Ont+H52A+iU4dSHPrzXWx7+X0szRrj77qCvFt/2fuwYxjQWgtNFQSrd7HrvZcp3bwNo87f0dtEU5Fhws25DsbOvZjCq78FrpzevychRK9J+BBiKDHCEGiGdh/L75+HCvi54tebIg0oo6FuP2/+4CaCLQHGzZ/P7j+/RjjXwphLL6J4xjXoGaMhuTAy6JVSEGyFltqjwaKKpsp9VJfuoaGyFF9NLe2NzYSbA2jNYfTg8ctJRpqJ9PHDKJ44C0/uaMzJueDOBXf+uc8OLIToN935/pbeLkLEO93UMQOuM38Y1esOEjj4AdaxV/W+bYhS0FwFdfup3vImm99+ldAuH2GPxlVPLMaUO4X8829m4z8eZed/3mLPc28BYJhBmRRooAe045eYAMOiwGFCd1qxJSeRPqKIlOw8MvJGkpQ5HN2VAymF4EjrXe1CiAFLznwIMZg0V7Pif66lfWMjoSwTWZNHMWLqhbjSC9HtLrAmglIE6krYv/Et6ivKMFss6LoJdI1wIEhbUxP+lnbCre2oxgB66/HLHYmj05hx7e04Z91x6lgXhgFN5VC7l7bq/QT9zYSDQZJSc7G4syAxDZIyI6FCzlwIMejIZRchhjKlCB9YydZXfsORrbtQNf7I2YcTaWB4dMzJiaiwQh0dOl4z6VgS7diTHDjcbrKHjSVj5Ez0tJHgKQazLQZvSAgRDyR8CCGOM8IQaDl6a4509XWknXWeESGE6A5p8yGEOE43gd0VuQkhxAAgU28KIYQQol9J+BBCCCFEv5LwIYQQQoh+JeFDCCGEEP1KwocQQggh+pWEDyGEEEL0KwkfQgghhOhXEj6EEEII0a8kfAghhBCiX0n4EEIIIUS/kvAhhBBCiH4l4UMIIYQQ/UrChxBCCCH61YCb1VYpBUSm5hVCCCFEfDj2vX3se/xMBlz4aGpqAiA/Pz/GlQghhBCiu5qamnC73WdcR1PnElH6kWEYlJeX43Q60TSt02M+n4/8/HxKS0txuVwxqjD2ZD9EyH6IkP1wnOyLCNkPEbIfjuuPfaGUoqmpiZycHHT9zK06BtyZD13XycvLO+M6LpdryB9IIPvhGNkPEbIfjpN9ESH7IUL2w3HR3hdnO+NxjDQ4FUIIIUS/kvAhhBBCiH4VV+HDZrPx4IMPYrPZYl1KTMl+iJD9ECH74TjZFxGyHyJkPxw30PbFgGtwKoQQQojBLa7OfAghhBAi/kn4EEIIIUS/kvAhhBBCiH4l4UMIIYQQ/WpAhY89e/Zw3XXXkZaWhsvl4oILLuCdd97peLyuro4FCxaQk5ODzWYjPz+fr3zlK2edB+aSSy5B07ROt1tvvTXab6fHorUf/H4/9957L2lpaTgcDj7+8Y9TVlYW7bfTY2fbD5s3b+a2224jPz+fhIQExo4dyy9+8YuzbjfejgeI3r4YbMcEwNe+9jWmTZuGzWZjypQp57TdeDsmorUfBuPxUFJSwrXXXovD4SAtLY2vfvWrBAKBM2433o4HiN6+iNYxMaDCx9VXX00oFOLtt99m/fr1TJkyhWuuuYbKykogMvrpddddx5IlS9izZw9//vOfWb58OV/60pfOuu27776bioqKjtvvfve7aL+dHovWfvj617/Oiy++yOLFi1m1ahXNzc1cc801hMPh/nhb3Xa2/bB+/XrS09P561//yvbt2/n+97/P/fffz69//euzbjuejgeI3r4YbMcERIZ4vuuuu/jkJz/ZrW3H0zERrf0w2I6HcDjM1VdfTUtLC6tWrWLx4sX861//4hvf+MZZtx1PxwNEb19E7ZhQA0RNTY0C1LvvvtuxzOfzKUAtX778tM/7xS9+ofLy8s647Ysvvlh97Wtf66tSoypa+6GxsVFZLBa1ePHijmVHjhxRuq6r119/vW+K70M93Q/33HOPuvTSS8+47Xg6HpSK3r4Y7MfEgw8+qCZPnnxO246nYyJa+2EwHg+vvvqq0nVdHTlypGOd559/XtlsNuX1ek+77Xg6HpSK3r6I5jExYM58pKamMnbsWJ599llaWloIhUL87ne/IzMzk2nTpnX5nPLycv79739z8cUXn3X7f/vb30hLS2P8+PF885vf7Jg9d6CJ1n5Yv349wWCQ+fPndyzLyclhwoQJfPDBB33+PnqrJ/sBwOv14vF4zrr9eDkeIHr7YqgcE+cqXo6JaO2HwXg8rF69mgkTJpCTk9PxvCuvvBK/38/69evPuP14OR4gevsimsfEgJlYTtM0li1bxnXXXYfT6UTXdTIzM3n99ddJTk7utO5tt93Gyy+/TFtbG9deey1/+MMfzrjtT3/60xQXF5OVlcW2bdu4//772bx5M8uWLYviO+qZaO2HyspKrFYrKSkpnZZnZmZ2OlU7UHRnPxyzevVq/vGPf7B06dIzbjuejgeI3r4YCsfEuYqnYyJa+2EwHg+VlZVkZmZ2el5KSgpWq/WM7ymejgeI3r6I5jER9TMfDz300CkNd06+rVu3DqUU99xzDxkZGbz33nt89NFHXHfddVxzzTVUVFR02ubPfvYzNmzYwEsvvcT+/fu57777zljD3Xffzbx585gwYQK33norL7zwAsuXL2fDhg3RfOudDIT90BWlFJqm9dXbPKto7AeA7du3c9111/GDH/yAK6644ow1DITjAQbGvujKYDkmumMgHBMDYT90Jd6Ph65qP9t7GgjHAwyMfdGVPjkmenXR5hzU1NSonTt3nvHW1tamli9frnRdP+Xa04gRI9SiRYtOu/333ntPAaq8vPycazIM45TrWNEW6/3w1ltvKUDV19d3Wj5p0iT1gx/8oPdv8BxFYz9s375dZWRkqO9973s9qikWx4NSsd8Xg/mYUKp7bT5ONpg+I851PwzG4+GBBx5QkyZN6vR4fX29AtTbb799zjUNhs+InuyLaB4TUb/skpaWRlpa2lnXa21tBSI9OU6k6zqGYZz2eero1DR+v/+ca9q+fTvBYJDs7Oxzfk5vxXo/TJs2DYvFwrJly/jEJz4BQEVFBdu2bePxxx8/p/fQF/p6P2zfvp3LLruMO+64gx//+Mc9qikWxwPEfl8M1mOiLwzGz4izGYzHw+zZs/nxj39MRUVFx/+Wb775JjabrVvtYwbDZ0RP9kVUj4leRZc+VFNTo1JTU9WNN96oNm3apHbv3q2++c1vKovFojZt2qSUUmrp0qXqT3/6k9q6das6ePCgWrp0qRo/fry64IILOrZTVlamRo8erdasWaOUUmrfvn3qhz/8oVq7dm3Hc8aMGaOmTp2qQqFQTN7rmURrPyil1Je+9CWVl5enli9frjZs2KAuu+wyNXny5LjdD9u2bVPp6enq05/+tKqoqOi4VVdXd2wn3o8HpaK3L5QafMeEUkrt3btXbdy4UX3xi19Uo0aNUhs3blQbN25Ufr9fKRX/x0S09oNSg+94CIVCasKECeryyy9XGzZsUMuXL1d5eXnqK1/5Ssd24v14UCp6+0Kp6B0TAyZ8KKXU2rVr1fz585XH41FOp1Odf/756tVXX+14/O2331azZ89Wbrdb2e12NXLkSPWd73xHNTQ0dKxz8OBBBah33nlHKaVUSUmJuuiii5TH41FWq1UNHz5cffWrX1V1dXX9/O7OXTT2g1JKtbW1qa985SvK4/GohIQEdc0116iSkpJ+fGfdc7b98OCDDyrglFthYWHHOoPheFAqOvtCqcF3TCgV6SbZ1b44ePCgUmpwHBPR2A9KDc7j4fDhw+rqq69WCQkJyuPxqK985Suqvb294/HBcDwoFZ19oVT0jglNqaPn64UQQggh+sGAGedDCCGEEEODhA8hhBBC9CsJH0IIIYToVxI+hBBCCNGvJHwIIYQQol9J+BBCCCFEv5LwIYQQQoh+JeFDCCGEEP1KwocQQggh+pWEDyGEEEL0KwkfQgghhOhXEj6EEEII0a/+P85X4BCB6xY0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CHANGE- do not use clipLines to find outcroppings.  Instead, find boundary counties with low neighborcounts\n",
    "print(\"Now finalize the map topology before any squish operations.  Plot countyPop/aDP\")\n",
    "preSquishCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "maxD = 0.\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"distances\")\n",
    "    if c not in cutCountyList:\n",
    "        plotPoly(countyGeom[c],0.2)\n",
    "        plotCenter(r3(countyPop[c]/aDP), countyGeom[c], 7 )\n",
    "        for cc in range(c+1, nCounties):\n",
    "            dist = getLongDist(countyCP[c],countyCP[cc],xScale)\n",
    "            if dist > maxD and cc not in cutCountyList:\n",
    "                maxD = dist\n",
    "                #print(c,cc,maxD)\n",
    "print(\"the max LAT-scaled distance between counties is\",r5(maxD))\n",
    "plotPoly(MAP)\n",
    "plotPoly(MAP.centroid.buffer(0.5*maxD))\n",
    "plt.show()\n",
    "inputD = float( input(\"enter user-picked max district diameter, or 0 to accept \"+str(r5(maxD))+\" \") )\n",
    "if inputD > 0:\n",
    "    maxD = inputD\n",
    "neighborCountyList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"neighbors\")\n",
    "    for cc in uncutCountyList:\n",
    "        if cc > c and cc not in cutCountyList:  \n",
    "            if countyGeom[c].intersects(countyGeom[cc]) :\n",
    "                neighborCountyList[c].append(cc)\n",
    "                neighborCountyList[cc].append(c)\n",
    "print(\"I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\")\n",
    "hasFewNeighborCs, has1neighborCs = list(), list()\n",
    "plotPoly(MAP,0.15)\n",
    "for c in uncutCountyList:\n",
    "    if len(neighborCountyList[c]) == 2:\n",
    "        hasFewNeighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.2,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.5)\n",
    "    if len(neighborCountyList[c]) < 2:\n",
    "        has1neighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.1,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d63e9f85-336f-4e38-8ed7-75035e9845f4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continuing from above, develop corner county blocks from each low-pop corner county until it hits 0.125 of 769364\n",
      "checking if county 43 with pop 82874.0 and 1 or 2 neighbors is less pop than 96170\n",
      "checking if county 44 with pop 90352.0 and 1 or 2 neighbors is less pop than 96170\n",
      "checking if county 51 with pop 959107.0 and 1 or 2 neighbors is less pop than 96170\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below I plot these again by county no, with faded neighboring counties\n",
      "0   [43]\n",
      "1   [44]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are 769364.269 96170\n",
      "let's decide whether to delete, accept, or modify list [43] with pop 82874.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's decide whether to delete, accept, or modify list [44] with pop 90352.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) 1\n",
      "enter 1 if you want to manually add another corner-county cluster 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our final pops and fused-county lists are...\n",
      "82874.0 [43]\n",
      "90352.0 [44]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#copied 2/17/24 from CO\n",
    "\n",
    "aDP = float(statePop)/nDistricts\n",
    "maxCCBratio = 1./8.  #adjustable max fraction of district pop for corner county blocks.  Let's try 3/16\n",
    "maxCCBpop = maxCCBratio * aDP\n",
    "print(\"continuing from above, develop corner county blocks from each low-pop corner county until it hits\",r3(maxCCBratio),\"of\",int(aDP) )\n",
    "CCBlist, CCBpop, passThruList = list(), list(), list()  #\"waiveThru\" counties get chance to join a cluster even if high pop\n",
    "nFuses = 0\n",
    "for c in has1neighborCs:\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and only 1 neighbor is less pop than\",int(maxCCBpop),\"vs districtPop\",int(aDP) )\n",
    "    if countyPop[c] > maxCCBpop:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        nc = neighborCountyList[c][0]\n",
    "        plotPoly(countyGeom[nc],0.5)\n",
    "        plotPoly(MAP,0.2)\n",
    "        print(\"county\",c,\"has pop\",countyPop[c],\"and its only neighbor has pop\",countyPop[nc],\"vs districtPop\",aDP,\". Default = keep un-fused\")\n",
    "        print(\"enter 0 to keep\",c,\"as an unfused collection of units, 1 to fuse as one unit and stop, 2 to force-add at least the next closest county\")\n",
    "        fuseChoice = input(\"0, 1, or 2.  Any other input taken as a zero\")\n",
    "        if fuseChoice == 0:\n",
    "            keepUnfused = True\n",
    "            break\n",
    "        elif int(fuseChoice) == 1:\n",
    "            CCBlist.append([c])\n",
    "            CCBpop.append(countyPop[c])  \n",
    "            hasFewNeighborCs.append(c)  #this adds this [c] to the final list of fused units, but will fail to find more in below\n",
    "        elif int(fuseChoice) == 2:\n",
    "            CCBlist.append([c,nc ] )\n",
    "            CCBpop.append(countyPop[c] + countyPop[nc])\n",
    "            hasFewNeighborCs.append(c)\n",
    "            passThruList.append(c)   #pass on thru to the below 2neighbor logic even if too high-pop to qualify\n",
    "    else:\n",
    "        hasFewNeighborCs.append(c)  #normal case; we'll handle in next, more general, for loop            \n",
    "        \n",
    "for c in hasFewNeighborCs:\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and 1 or 2 neighbors is less pop than\",int(maxCCBpop) )\n",
    "    if countyPop[c] < maxCCBpop :\n",
    "        CCBlist.append([c])\n",
    "        CCBpop.append(countyPop[c])\n",
    "        CCBno = CCBlist.index([c])\n",
    "    if c in passThruList:\n",
    "        CCBno = CCBlist.index([c,neighborCountyList[c][0] ])\n",
    "    if countyPop[c] < maxCCBpop or c in passThruList:            \n",
    "        CCBstarter = c\n",
    "        canAdd = True\n",
    "        while canAdd:\n",
    "            nbrSet = set()\n",
    "            for cc in CCBlist[CCBno]:\n",
    "                nbrSet = ( nbrSet.union(set(neighborCountyList[cc])) ).difference(set(CCBlist[CCBno]))\n",
    "            nPop = [countyPop[cc] for cc in nbrSet]\n",
    "            nDist = [countyGeom[cc].centroid.distance(countyGeom[CCBstarter].centroid) for cc in nbrSet]\n",
    "            idx = np.argsort(nDist)\n",
    "            nbrList, newList = list(nbrSet), list()\n",
    "            for i in range(len(nDist)):  #add counties, closest to starter that will fit until over\n",
    "                cc = nbrList[idx[i]]\n",
    "                if CCBpop[CCBno] + countyPop[cc] < maxCCBpop:\n",
    "                    newList.append(cc)\n",
    "                    CCBlist[CCBno].append(cc)\n",
    "                    CCBpop[CCBno] += countyPop[cc]\n",
    "                    #print(\"adding county\",cc,\"with pop\",countyPop[cc],\"to list started by\",CCBstarter,\".Cluster pop now\",CCBpop[CCBno]  )\n",
    "            if newList == list():  #no eligible neighbor counties found; stop\n",
    "                canAdd = False\n",
    "                for cc in CCBlist[CCBno]:\n",
    "                    plotPoly(countyGeom[cc])\n",
    "                    plotCenter(CCBno,countyGeom[cc])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "                    \n",
    "print(\"Below I plot these again by county no, with faded neighboring counties\")\n",
    "plotPoly(MAP,0.15)\n",
    "for L in CCBlist:\n",
    "    print(CCBlist.index(L),\" \",L)\n",
    "    for c in L:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        for cc in list(set(neighborCountyList[c]).difference(L) ):\n",
    "            plotPoly(countyGeom[cc],0.4)\n",
    "            plotCenter(cc,countyGeom[cc],8)\n",
    "plt.show()\n",
    "rejectList = list()\n",
    "print(\"Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are\",r3(aDP), int(maxCCBpop))\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if L[0] not in passThruList:  #if we forced the fused-counties list above, don't give option here to modify\n",
    "        print(\"let's decide whether to delete, accept, or modify list\",L,\"with pop\",CCBpop[i] )\n",
    "        isOK = input(\"enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list)\")\n",
    "        if not (isOK == 1 or isOK == str(1)) :\n",
    "            if isOK == 0 or isOK == str(0) :\n",
    "                rejectList.append(L)\n",
    "            else:\n",
    "                notGood = True\n",
    "                while notGood:       \n",
    "                    Lnew = ast.literal_eval(isOK)        \n",
    "                    if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                        notGood = False\n",
    "                    else:\n",
    "                        print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                        isOK = input(\"Try again here \")\n",
    "                print(\"OK, I will replace\",L,\"with\",Lnew)\n",
    "                CCBpop[i] = np.sum( [countyPop[c] for c in Lnew] )\n",
    "                CCBlist[i] = Lnew.copy()\n",
    "for L in rejectList:\n",
    "    del CCBlist[CCBlist.index(L)]\n",
    "    del CCBpop[ CCBlist.index(L)]\n",
    "stillAdding = True\n",
    "while stillAdding:\n",
    "    addMore = input(\"enter 1 if you want to manually add another corner-county cluster\")\n",
    "    if int(addMore) != 1:\n",
    "        stillAdding = False\n",
    "    else:\n",
    "        isOK = input(\"Type in a [county list], where the corner county is first in the list.  Or type 0 to stop\")\n",
    "        if isOK == 0 or isOK == str(0) :\n",
    "            print(\"OK, we'll stop adding corner clusters\")\n",
    "            stillAdding = False\n",
    "        else:\n",
    "            notGood = True\n",
    "            while notGood:       \n",
    "                Lnew = ast.literal_eval(isOK)        \n",
    "                if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                    notGood = False\n",
    "                    for L in CCBlist:\n",
    "                        if set(L).intersection(set(Lnew)) != set(list()) :\n",
    "                            notGood = True\n",
    "                            print(\"OOPS! The following inputted counties are already in\",L,\":\",set(L).intersection(set(Lnew)) )\n",
    "                            isOK = input(\"Try again here \")\n",
    "                    if notGood == False:\n",
    "                        CCBlist.append(Lnew)\n",
    "                        CCBpop.append( np.sum( [countyPop[c] for c in Lnew] ) )\n",
    "                else:\n",
    "                    print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                    isOK = input(\"Try again here \")\n",
    "print(\"Our final pops and fused-county lists are...\")\n",
    "allFusedCounties = list()\n",
    "CCBgeom = [dummyPoly for L in CCBlist ]\n",
    "CCBcp = list()\n",
    "for i, L in enumerate(CCBlist):\n",
    "    CCBcp.append( getHDcp([countyCP[c] for c in L], [countyPop[c] for c in L], [ j for j in range(len(L)) ] ) )\n",
    "    print(CCBpop[i],L)\n",
    "    pops, CPs = list(), list()\n",
    "    for c in L:\n",
    "        if c == L[0]:\n",
    "            CCBgeom[i] = countyGeom[c]\n",
    "        else:\n",
    "            CCBgeom[i] = CCBgeom[i].union(countyGeom[c])\n",
    "        allFusedCounties.append(c)\n",
    "        pops += countyPop[c]       #  IS THIS EVEN USED?\n",
    "        CPs.append(countyCP[c])   #  IS THIS EVEN USED?\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(i,countyGeom[c])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "206c9372-8ec8-4190-93ed-04df286466e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now identify the border counties + border fused units, and interior small counties with pop < 15387\n",
      "We defined a total of 4 unit (but not corner-cluster) counties in the map, excluding the cut-out districts\n"
     ]
    }
   ],
   "source": [
    "maxWholeBorderCountyPop = 0.02 * aDP   #to encourage contiguity, border counties > 0.02 aDP will be forced whole\n",
    "maxInteriorBorderCountyPop = 0.02 * aDP\n",
    "print(\"now identify the border counties + border fused units, and interior small counties with pop <\", int(maxInteriorBorderCountyPop))\n",
    "unitCounties, borderCounties = list(), list()\n",
    "for c in uncutCountyList:\n",
    "    if countyGeom[c].intersects(MAP.exterior):\n",
    "        borderCounties.append(c)\n",
    "        if c not in allFusedCounties:\n",
    "            if countyPop[c] < maxWholeBorderCountyPop:\n",
    "                unitCounties.append(c)\n",
    "    else:\n",
    "        if countyPop[c] < maxInteriorBorderCountyPop:\n",
    "            unitCounties.append(c)\n",
    "print(\"We defined a total of\",len(unitCounties),\"unit (but not corner-cluster) counties in the map, excluding the cut-out districts\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e0ff719c-59dc-4827-a522-11bd352f7524",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this state, do we have a lot of populated fragmented VTDs?\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'nFragmentedVTDs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[17], line 6\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m c \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m allFusedCounties \u001b[38;5;129;01mand\u001b[39;00m c \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m unitCounties: \n\u001b[0;32m      5\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m vtdGeom[v]\u001b[38;5;241m.\u001b[39mgeom_type \u001b[38;5;241m!=\u001b[39m dummyPoly\u001b[38;5;241m.\u001b[39mgeom_type :  \u001b[38;5;66;03m#a multipoly vtd-based unit\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m             nFragmentedVTDs \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[0;32m      7\u001b[0m             fragmentedVTDs\u001b[38;5;241m.\u001b[39mappend(v)\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mlen\u001b[39m(fragmentedVTDs),\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfragmented VTDs out of\u001b[39m\u001b[38;5;124m\"\u001b[39m,nVTDs)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'nFragmentedVTDs' is not defined"
     ]
    }
   ],
   "source": [
    "print(\"For this state, do we have a lot of populated fragmented VTDs?\")\n",
    "nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "print(len(fragmentedVTDs),\"fragmented VTDs out of\",nVTDs)\n",
    "fragPop = [tractPop[v] for v in fragmentedVTDs]\n",
    "plt.hist(fragPop)\n",
    "plt.show()\n",
    "print(\"FYI, here are the locations of fragmented VTDs with pop > 8000\")\n",
    "for v in fragmentedVTDs:\n",
    "    if tractPop[v] > 8000:\n",
    "        plotPoly(vtdGeom[v])\n",
    "plotPoly(MAP,0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "075faa01-7c09-475c-a5ba-c1939c8d8660",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7211\n",
      "<class 'geopandas.geoseries.GeoSeries'> <class 'geopandas.geoseries.GeoSeries'>\n"
     ]
    }
   ],
   "source": [
    "print(len(vtdGeom))\n",
    "print(type(vtdGeom),type(fragVTDgeom))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "71d771f8-dbe2-48f9-a702-9f66a62d1174",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\n",
      "I split up 426 fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\n",
      "looking for surrounds, now working on vtd 8\n",
      "WARNING. vtd frag 110 in county 13 intersected county 19 but had no intracounty neighbors\n",
      "looking for surrounds, now working on vtd 765\n",
      "looking for surrounds, now working on vtd 1532\n",
      "looking for surrounds, now working on vtd 2250\n",
      "looking for surrounds, now working on vtd 2752\n",
      "looking for surrounds, now working on vtd 3252\n",
      "looking for surrounds, now working on vtd 3754\n",
      "looking for surrounds, now working on vtd 4254\n",
      "looking for surrounds, now working on vtd 4755\n",
      "looking for surrounds, now working on vtd 5278\n",
      "looking for surrounds, now working on vtd 5778\n",
      "looking for surrounds, now working on vtd 6281\n",
      "looking for surrounds, now working on vtd 6781\n",
      "On loop 2 for county 4  we found that vtd frag 3314 now has only 1 neighbor 7521\n",
      "After surround checks, we appear to have 6795 building blocks defined. We captured 484 surrounded VTDs\n",
      "working on unit neighbor lists for county 0\n",
      "WARNING - unit 42  = vtd 110 in county 13 has only 1 neighbor= [30] . Optional triage in next block\n",
      "working on unit neighbor lists for county 20\n",
      "working on unit neighbor lists for county 40\n",
      "working on unit neighbor lists for county 60\n",
      "All done creating unit lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/2/24 - split up all multipoly VTDs into fragments, divvy pop by area\n",
    "unitPop, unitCP, unitGeom, nbrUnitGeom, allUnits = list(), list(), list(), list(), list()\n",
    "vtdUnits, countyUnits, borderUnits = list(),list(),list()\n",
    "countyUnitList = [list() for c in range(nCounties) ]\n",
    "surroundedVTDs, surrounders = list(), list()  #vtds that are surrounded by another vtd - problem for later enclaves, xfer their pops to surrounders\n",
    "#These surrounded VTDs don't get transferred to the unit list\n",
    "print(\"Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\")\n",
    "for c in unitCounties:\n",
    "    unitCP.append(countyCP[c])\n",
    "    unitPop.append(countyPop[c])\n",
    "    unitGeom.append(   countyGeom[c] )\n",
    "    nbrUnitGeom.append(countyGeom[c] )\n",
    "    countyUnits.append(c)\n",
    "    allUnits.append(c+0.5)  #use non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nVTDneighbors = [0 for v in range(nVTDs)] #this loop -- just checking for surrounded VTDs\n",
    "lastVTDneighbor = [-999 for v in range(nVTDs)]\n",
    "\n",
    "nFragmentedVTDs,fragmentedVTDs, coastalFragmentedVTDs = 0, list(), list()  #a prev version treated coastal separately\n",
    "fragVTDgeom, parentVTDno, fragVTDpop = vtdGeom.to_list(), [v for v in range(nVTDs)], tractPop.to_list()  #these lists will expand to include vtd fragments \n",
    "origVTDgeom = vtdGeom.copy() #we create a parallel fragVTDgeom list which grabs the 1st fragment, then append fragments to the total list\n",
    "origVTDpop = tractPop.copy()\n",
    "VTDchildren = [[v] for v in range(nVTDs)]  #the list of all fragment numbers associated with the original VTD, including ones that get surrounded\n",
    "countyFragVTDset = [set(countyTractList[c]) for c in range(nCounties)]\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "            geos, areas = [geo for geo in vtdGeom[v].geoms ], [geo.area for geo in vtdGeom[v].geoms]\n",
    "            for i, geo in enumerate(geos):\n",
    "                if i == 0:\n",
    "                    fragVTDgeom[v] = geo\n",
    "                    fragVTDpop[v] = tractPop[v] * areas[i] / np.sum(areas)  #overwrite, then distribute balance below\n",
    "                else:\n",
    "                    fNo = len(fragVTDgeom)\n",
    "                    fragVTDgeom.append(geo)\n",
    "                    fragVTDpop.append( tractPop[v] * areas[i] / np.sum(areas) )\n",
    "                    parentVTDno.append(v)\n",
    "                    countyFragVTDset[c].add(fNo )\n",
    "                    VTDchildren[v].append(fNo)\n",
    "                    nVTDneighbors.append(0)\n",
    "                    lastVTDneighbor.append(-999)\n",
    "                    \n",
    "print(\"I split up\",nFragmentedVTDs,\"fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\")\n",
    "\n",
    "debugV = -7616\n",
    "for kk,V in enumerate(populatedTractList):\n",
    "    if kk%500 == 0:\n",
    "        print(\"looking for surrounds, now working on vtd\",V)\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties:\n",
    "        for v in VTDchildren[V]:\n",
    "            for vv in countyFragVTDset[c].difference({v}) :\n",
    "                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                    nVTDneighbors[v] +=1\n",
    "                    lastVTDneighbor[v] = vv\n",
    "                    if v == debugV:\n",
    "                        print(\"I just added neighbor\",vv,\"to magicV\",v)\n",
    "                #if nVTDneighbors[v] >= 4:  #Enough to have >=2 even after surrounds.  Will do full neighbor list later\n",
    "                #    break\n",
    "            if nVTDneighbors[v] < 4:  #OK if a vtd has only 1 intracounty neighbor IF it touches another county, it's not surrounded\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if fragVTDgeom[v].intersects(countyGeom[cc]):\n",
    "                        if cc not in unitCounties + allFusedCounties:\n",
    "                            for vv in countyFragVTDset[cc] :\n",
    "                                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                                    nVTDneighbors[v] +=1\n",
    "                                    lastVTDneighbor[v] = vv\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                print(\"WARNING. vtd frag\",v,\"in county\",c,\"intersected county\",cc,\"but had no intracounty neighbors\")\n",
    "                                plotPoly(fragVTDgeom[v],2)\n",
    "                                plotCenter(\"iso\",fragVTDgeom[v])\n",
    "                                plotPoly(countyGeom[cc])\n",
    "                        else:\n",
    "                            nVTDneighbors[v] +=1\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                raise Exception(\"neighborless vtd fragment\",v,\"neighbors a unit or fused county\",cc)                                \n",
    "            if v == debugV:\n",
    "                print(v,\"has\",nVTDneighbors[v],\"neighbors.  its last is\",lastVTDneighbor[v])\n",
    "                \n",
    "for loop in range(3): #to catch surrounds of surrounds\n",
    "    for V in populatedTractList:\n",
    "        c = countyNo[V]\n",
    "        if c not in allFusedCounties and c not in unitCounties: \n",
    "            for v in VTDchildren[V]:\n",
    "                if nVTDneighbors[v] == 1:\n",
    "                    vv = lastVTDneighbor[v]\n",
    "                    if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                        for sNo, s in enumerate(surroundedVTDs):\n",
    "                            if surrounders[sNo] == v:\n",
    "                                surrounders[sNo] = vv\n",
    "                    surroundedVTDs.append(v)\n",
    "                    surrounders.append(vv)\n",
    "                    nVTDneighbors[vv] -=1\n",
    "                    nVTDneighbors[v] -=1 #in case this surrounder becomes a later surroundee\n",
    "                    fragVTDpop[vv] += fragVTDpop[v]\n",
    "                    fragVTDpop[v] = 0\n",
    "                    if lastVTDneighbor[vv] == v:  #find another neighbor in case this surrounder is also a surroundee in loop 2\n",
    "                        for vvv in countyFragVTDset[c]:\n",
    "                            if vvv not in [v,vv]:\n",
    "                                if fragVTDgeom[vvv].intersects(fragVTDgeom[vv]):\n",
    "                                    lastVTDneighbor[vv] = vvv\n",
    "                    if lastVTDneighbor[vv] == v:\n",
    "                        print(\"WARNING.  We did not find another neighbor of surrounder\",vv,\"other than surroundee\",v)\n",
    "                    if loop > 0:\n",
    "                        print(\"On loop\",loop+1,\"for county\",c,\" we found that vtd frag\",v,\"now has only 1 neighbor\",vv)\n",
    "            \n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] >1:\n",
    "                unitCP.append(fragVTDgeom[v].centroid)\n",
    "                unitGeom.append(fragVTDgeom[v])\n",
    "                unitPop.append(fragVTDpop[v])   #for now, ratio pop by area, not block decomposition\n",
    "                vtdUnits.append(v)   #if a border unit, we'll catch in below big loop, after checking for surround\n",
    "                allUnits.append(v)\n",
    "#for V in populatedTractList:\n",
    "#    c = countyNo[V]\n",
    "#    if c not in allFusedCounties and c not in unitCounties:  \n",
    "#        for v in VTDchildren[V]: \n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] == 1:  #a surrounded VTD, transfer its pop to surrounder unit.  Don't bother with shifting centerpoints\n",
    "                print(\"somehow we missed 1-neighbor vtd frag\",v,\" with last vtd frag nbr\",lastVTDneighbor[v])\n",
    "            if nVTDneighbors[v] == 0 and v not in surroundedVTDs:\n",
    "                print(\"WARNING. vtd\",v,\"had no found neighbors\")\n",
    "\n",
    "for i, L in enumerate(CCBlist):\n",
    "    unitCP.append( CCBcp[i] )\n",
    "    unitPop.append(CCBpop[i])\n",
    "    unitGeom.append(   CCBgeom[i])\n",
    "    nbrUnitGeom.append(CCBgeom[i])\n",
    "    allUnits.append(i+0.25)  #use alt non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nUnits = len(allUnits)\n",
    "print(\"After surround checks, we appear to have\",nUnits,\"building blocks defined. We captured\",len(surroundedVTDs),\"surrounded VTDs\")\n",
    "\n",
    "unitNbrs = [list() for u in range(nUnits)]\n",
    "countyUnitList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    for v in countyFragVTDset[c]:\n",
    "        if v in allUnits:\n",
    "            countyUnitList[c].append(allUnits.index(v))\n",
    "\n",
    "sketchyList = list()\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"working on unit neighbor lists for county\",c)\n",
    "    if c not in allFusedCounties:  #see a separate loop below for cluster-cluster neighbors\n",
    "        if c in unitCounties:    \n",
    "            u = allUnits.index(c+0.5)\n",
    "            for cc in neighborCountyList[c]:\n",
    "                if cc not in allFusedCounties:\n",
    "                    if cc in unitCounties:\n",
    "                        unitNbrs[u].append(allUnits.index(cc+0.5))  #we'll catch the complement in the cc county's loop\n",
    "                    else:\n",
    "                        for uu in countyUnitList[cc] :\n",
    "                            if countyGeom[c].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu)\n",
    "                                unitNbrs[uu].append(u)\n",
    "            for i,geo in enumerate(CCBgeom):\n",
    "                if geo.intersects(countyGeom[c]):\n",
    "                    unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                    unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "        else:  #non-unit county      \n",
    "            for u in countyUnitList[c] :\n",
    "                for uu in list( set(countyUnitList[c]).difference({u}) ) :\n",
    "                    if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                        unitNbrs[u].append(uu )  #will catch complement later in this county's loop\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if cc not in allFusedCounties and cc not in unitCounties: #see an above block for handling unitCounties\n",
    "                        for uu in countyUnitList[cc]:\n",
    "                            if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu )\n",
    "\n",
    "            for u in countyUnitList[c] :\n",
    "                for i,geo in enumerate(CCBgeom):\n",
    "                    if geo.intersects(unitGeom[u]):\n",
    "                        unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                        unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "                if len(unitNbrs[u]) == 0:\n",
    "                    print(\"ERROR - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has no neighbors\")\n",
    "                if len(unitNbrs[u]) == 1:  #after fragmentation, the piece we selected is surrounded.  KISS - take on this neighbor's neighbors                       \n",
    "                    print(\"WARNING - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has only 1 neighbor=\",unitNbrs[u],\". Optional triage in next block\")\n",
    "                    sketchyList.append([u,unitNbrs[u][0] ] )\n",
    "\n",
    "CCBunits = CCBlist.copy()                        \n",
    "for i, geo in enumerate(CCBgeom):  #this loop: only cluster-cluster intersections.  Cluster-vtd and cluster-county neighbors already found above.\n",
    "    for ii in range(i+1,len(CCBgeom) ) :\n",
    "        if geo.intersects(CCBgeom[ii]):\n",
    "            unitNbrs[allUnits.index(i+0.25) ].append(allUnits.index(ii+0.25) ) #all CCB-county, CCB-vtd intersxns already covered\n",
    "            unitNbrs[allUnits.index(ii+0.25)].append(allUnits.index(i+0.25) )\n",
    "print(\"All done creating unit lists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "3ed5ebb3-f0ab-42c1-b0f1-34ebf9124dc0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "u = 42\n",
    "plotPoly(unitGeom[u])\n",
    "for c in range(nCounties):\n",
    "    if u in countyUnitList[c]:\n",
    "        plotPoly(countyGeom[c],0.1)\n",
    "        plotCenter(\"county\"+str(c),countyGeom[c])\n",
    "for uu in unitNbrs[u]:\n",
    "    plotPoly(unitGeom[uu],0.5)\n",
    "    plotCenter(uu,unitGeom[uu])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8378940d-4387-4188-8ae1-a62010500ecb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now build the border topology\n",
      "counties and clusters done, now working on (frag) vtd-based units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Now build the border topology\")\n",
    "borderUnits, borderType = list(), list()\n",
    "for c in borderCounties:\n",
    "    if c in unitCounties:\n",
    "        borderUnits.append(allUnits.index(c+0.5))\n",
    "        borderType.append(\"county\")\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if CCBgeom[i].intersects(MAP.exterior):  #should always be the case\n",
    "        borderUnits.append(allUnits.index(i+0.25))\n",
    "        borderType.append(\"cluster\")\n",
    "print(\"counties and clusters done, now working on (frag) vtd-based units\")\n",
    "for c in borderCounties:\n",
    "    if c not in unitCounties + allFusedCounties:\n",
    "        for u in countyUnitList[c]:\n",
    "            if unitGeom[u].intersects(MAP.exterior):\n",
    "                borderUnits.append(u)\n",
    "                borderType.append(\"vtd\")\n",
    "    \n",
    "for i,b in enumerate(borderUnits):\n",
    "    plotPoly(unitGeom[b])\n",
    "    if borderType[i] == \"cluster\":\n",
    "        j = int(allUnits[b])\n",
    "        for c in CCBlist[j]:\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "            plotCenter(\"fused\",countyGeom[c], 6)\n",
    "for c in unitCounties:\n",
    "    plotPoly(countyGeom[c],0.4)\n",
    "    plotCenter(\"unit\",countyGeom[c],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "937d923d-9e95-45e9-9aef-77cb4377781d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking that the list of borderUnits is contiguous all around\n",
      "number of border units = 169\n"
     ]
    }
   ],
   "source": [
    "print(\"checking that the list of borderUnits is contiguous all around\")\n",
    "print(\"number of border units =\",len(borderUnits))\n",
    "for u in borderUnits:\n",
    "    isUnbroken, smallPieceList = isContiguous(list(set(borderUnits).difference({u})),unitNbrs)\n",
    "    if not isUnbroken:\n",
    "        print(\"border is discontig if we skip\",u)\n",
    "        for uu in smallPieceList:\n",
    "            plotPoly(unitGeom[uu],0.5)\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotPoly(countyGeom[29],0.2)\n",
    "        plotPoly(countyGeom[55], 0.2)\n",
    "        plt.show()\n",
    "borderSet = set(borderUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "93603f77-77fa-4453-b54b-5ea07fcb6721",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here, we identify 'border' units that aren't needed to define the map border\n",
      "Bueno! no 'border units' that could be eliminated from the border set\n"
     ]
    }
   ],
   "source": [
    "print(\"here, we identify 'border' units that aren't needed to define the map border\")\n",
    "canBeRemoved, powerNeighbors = set(), set()\n",
    "for u in borderUnits:\n",
    "    uNeighbors = list(borderSet.intersection(set(unitNbrs[u])) )\n",
    "    if len(uNeighbors) < 2:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        uu = uNeighbors[0]\n",
    "        otherBorderNeighbors = set(unitNbrs[uu]).intersection(borderSet).difference({u})\n",
    "        if len(otherBorderNeighbors) > 1:  #this neighbor of our 1-border-neighbor border unit makes a border chain without the problem border unit\n",
    "            canBeRemoved.add(u)\n",
    "            powerNeighbors.add(uu)\n",
    "        plotCenter(uu,unitGeom[uu],6)\n",
    "        plotPoly(unitGeom[uu],0.5)\n",
    "        for uuu in set(unitNbrs[uu]).intersection(borderSet).difference({u}):\n",
    "            plotPoly(unitGeom[uuu])\n",
    "            plotCenter(str(uuu)+\"=N of\"+str(uu),unitGeom[uuu])\n",
    "        for uuu in set(unitNbrs[u]).difference(borderSet):\n",
    "                plotCenter(uuu+0.1,unitGeom[uuu],6)\n",
    "                plotPoly(unitGeom[uuu],0.1)\n",
    "        c = countyNo[allUnits[u]]\n",
    "        #plotPoly(countyGeom[c] )\n",
    "        #plotCenter(c, countyGeom[c])\n",
    "        plt.show()\n",
    "if len(canBeRemoved) == 0:\n",
    "    print(\"Bueno! no 'border units' that could be eliminated from the border set\")\n",
    "else:\n",
    "    print(canBeRemoved,\"is the list of 1-neighbor 'border' units that can be eliminated from the border set\")\n",
    "    print(\"... as they are enveloped on the border; the enveloping border unit has two other map-border neighbors\")\n",
    "    yesRemove = input(\"enter 1 to remove these from the list of border units\")\n",
    "    if int(yesRemove) == 1:\n",
    "        canRemove = True\n",
    "        for uu in powerNeighbors:\n",
    "            testSet = (borderSet.difference(canBeRemoved)).difference({uu})\n",
    "            if not isContiguous(list(testSet),unitNbrs):\n",
    "                canRemove = False\n",
    "                print(\"We can't complete the border if unit\",uu,\"is not included in the ring\")\n",
    "        if canRemove:\n",
    "            borderSet = borderSet.difference(canBeRemoved)\n",
    "            borderList = list(borderSet)\n",
    "            borderUnits = list(borderSet)\n",
    "            print(\"Success! we removed\",canBeRemoved,\"from the list of borderUnits\")\n",
    "    else:\n",
    "        print(\"Fine, be that way.  Sheesh, I will keep them.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b13c68e3-33ed-48b3-acca-78012ce0bf61",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is the final border set\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "print(\"here is the final border set\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "173bb175-312b-4d08-a5b5-80a1d5d70416",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(isContiguous(borderUnits,unitNbrs)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5acda29e-28a9-474b-9706-20a79121a07e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the original HD polygon shape assignment csv, e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have read back in the original HD shapes\n",
      "County numbers not in input file. Now I will assign each HD center to a county based on the county geoms\n",
      "Ready to capture unit centroids based on these HD poly shapes; see next block\n"
     ]
    }
   ],
   "source": [
    "#OPTION 1 - we already have an ensemble of HDpoly's and their weights\n",
    "#read them in\n",
    "#determine each cluster's required area fraction capture to get 1.00 cluster use across the HD set\n",
    "#then determine total pop (including cluster in/out) for each HD, and total unit use\n",
    "#then move into triage\n",
    "infilename = input(\"enter the original HD polygon shape assignment csv, \"+ \n",
    "                   \"e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv\")\n",
    "shapeDF = pd.read_csv(infilename)\n",
    "HDvPop = shapeDF[\"HD-pop\"].to_list()\n",
    "HDweight = shapeDF['HDweight'].to_list() #shapeDF['HDwt'].to_list() or #shapeDF['HDweight'].to_list()\n",
    "HDarea = shapeDF['HDarea'].to_list()\n",
    "#tractPop = shapeDF['tractPop'].to_list()   #we will use vtd-mapped pops instead\n",
    "nHDs = len(shapeDF)\n",
    "hdCPx = shapeDF['centroid x'].to_list()   #note: these may be translated from outcroppings into a convex representation of the map\n",
    "hdCPy = shapeDF['centroid y'].to_list()\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs) ]\n",
    "HDradius0 = shapeDF['HDradius0'].to_list()\n",
    "HDradius1 = shapeDF['HDradius1'].to_list()\n",
    "HDradius2 = shapeDF['HDradius2'].to_list()\n",
    "HDradius3 = shapeDF['HDradius3'].to_list()\n",
    "HDangle0 = shapeDF['HDangle0'].to_list()\n",
    "HDangle1 = shapeDF['HDangle1'].to_list()\n",
    "HDangle2 = shapeDF['HDangle2'].to_list()\n",
    "HDangle3 = shapeDF['HDangle3'].to_list()\n",
    "HDradius, HDangle = list(), list()\n",
    "for t in range(nHDs):\n",
    "    HDradius.append( [HDradius0[t],HDradius1[t],HDradius2[t],HDradius3[t] ] )\n",
    "    HDangle.append(  [HDangle0[t], HDangle1[t], HDangle2[t], HDangle3[t]  ] )\n",
    "print(\"I have read back in the original HD shapes\")\n",
    "popHDlist = list()\n",
    "HDpoly = [dummyPoly for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if HDweight[t] > 0.000001:\n",
    "        popHDlist.append(t)\n",
    "        HDpoly[t] = buildArcPoly(hdCP[t],HDradius[t], HDangle[t], xScale)\n",
    "if \"pigs\" == \"can fly\": #countyNo in shapeDF.columns.values:\n",
    "    HDcountyNo = shapeDF['countyNo'].to_list()\n",
    "else:\n",
    "    print(\"County numbers not in input file. Now I will assign each HD center to a county based on the county geoms\")\n",
    "    HDcountyNo = [-999]*nHDs\n",
    "    for t in popHDlist:\n",
    "        for c, geom in enumerate(countyGeom):\n",
    "            if geom.contains(hdCP[t]):\n",
    "                HDcountyNo[t] = c\n",
    "                break\n",
    "    unassignedList = list()\n",
    "    for t in popHDlist:\n",
    "        if HDcountyNo[t] == -999:\n",
    "            unassignedList.append(t)\n",
    "    if len(unassignedList) > 0:\n",
    "        print(\"I found\",len(unassignedList),\"populd HDs whose centers fall outside vtd-based county lines.  Assign to closest county\")\n",
    "        for t in unassignedList:\n",
    "            minDist = maxD\n",
    "            for c in range(nCounties):\n",
    "                dist = countyGeom[c].distance(hdCP[t])\n",
    "                if dist < minDist:\n",
    "                    minDist = dist\n",
    "                    HDcountyNo[t] = c\n",
    "if len(unassignedList) > 0:\n",
    "    print(\"FYI, here are those manually assigned HDcenters to their counties\")\n",
    "    for t in unassignedList:\n",
    "        print(t,HDweight[t],\" is the HD row and its weight in the ensemble\")\n",
    "        plotPoly(hdCP[t].buffer(0.1))\n",
    "        plotCenter(int(HDvPop[t]),hdCP[t],6)\n",
    "        plotPoly(countyGeom[HDcountyNo[t]])\n",
    "        plotPoly(MAP,0.2)\n",
    "        plt.show()\n",
    "print(\"Ready to capture unit centroids based on these HD poly shapes; see next block\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2fb6d6e3-6157-4728-a6e5-ea22ede3de03",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OPTIONAL - write the unit topology to a file\n"
     ]
    }
   ],
   "source": [
    "date_ = \"04Mar24\"\n",
    "print(\"OPTIONAL - write the unit topology to a file\")   #New for FL 2/17/24 - include parent VTDs\n",
    "unitParentVTDno = [-999 for u in range(nUnits)]\n",
    "for u in range(nUnits):    \n",
    "    if allUnits[u] %1 == 0:\n",
    "        v = allUnits[u]\n",
    "        unitParentVTDno[u] = parentVTDno[v]\n",
    "\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        #for i,uu in enumerate(surrounders):  #no longer tracked\n",
    "         #   if u == uu:\n",
    "         #       unitVTDlist[u].append(surroundedVTDs[i])\n",
    "                \n",
    "unitNo = [u for u in range(nUnits)]\n",
    "unitCPx, unitCPy = [unitCP[u].x for u in range(nUnits)], [unitCP[u].y for u in range(nUnits)]\n",
    "onBorder = [0 for u in range(nUnits)]\n",
    "for u in borderUnits:\n",
    "    onBorder[u] = 1\n",
    "nbrDF = pd.DataFrame( {\"unitNo\":unitNo,\"centroid x\":unitCPx,\"centroid y\":unitCPy,\"unitPop\":unitPop,\"onBorder\":onBorder,\n",
    "                       \"neighborList\":unitNbrs, \"unitVTDlist\":unitVTDlist, \"unitParentVTDno\": unitParentVTDno} )\n",
    "outname = STATE+\"unitTopologies_\"+date_+\".csv\" \n",
    "outpath = \"state_map_files/\"+outname\n",
    "nbrDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c47fa1ff-2bed-4641-96e9-8dbdb7cb6da9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on cluster no 0 containing counties [43]\n",
      "halfway there! last use was 0.70986\n",
      "our final fractional capture area to normalize this cluster's usage is 0.38018 from 266 intersecting HDs\n",
      "working on cluster no 1 containing counties [44]\n",
      "halfway there! last use was 0.82415\n",
      "our final fractional capture area to normalize this cluster's usage is 0.31589 from 263 intersecting HDs\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing w/option 1 - set the cluster area thresholds to unitize cluster use\n",
    "#NOTE -- THIS MAY NOT BE RIGHT -- DEPENDS ON HOW WE GENERATED THE HDpoly's\n",
    "\n",
    "CCBuse, CCBareaFrac, CCBarea = [0. for CCB in CCBlist], [0.5 for CCB in CCBlist], [geo.area for geo in CCBgeom]\n",
    "for j,geo in enumerate(CCBgeom):\n",
    "    nIntersections = 0\n",
    "    print(\"working on cluster no\",j,\"containing counties\",CCBlist[j] )\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    HDfrac = [ 0. for t in range(nHDs) ]\n",
    "    for t in range(nHDs):\n",
    "        if HDcountyNo[t] in CCBlist[j]:\n",
    "            HDfrac[t] = 1.\n",
    "        else:\n",
    "            HDfrac[t] = HDpoly[t].intersection(geo).area / CCBarea[j]\n",
    "    idx = np.argsort(HDfrac)\n",
    "    i = nHDs\n",
    "    while CCBuse[j] < 1.000 - 0.5 * nDistricts*np.average(HDweight) and i > 0:\n",
    "        i -=1\n",
    "        nIntersections +=1\n",
    "        t = idx[i]\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        if CCBuse[j] > 0.5 and CCBuse[j] - HDweight[t] * nDistricts < 0.5:\n",
    "            print(\"halfway there! last use was\",r5(HDfrac[t]))\n",
    "            plotPoly(HDpoly[t].intersection(MAP.buffer(1)),0.2)\n",
    "        #origHDlist[t].append(unitNo)  #postpone this until main loop\n",
    "        #origHDpop[t] += CCBpop[j]     #postpone until main\n",
    "    if i <= 1:\n",
    "        print(\"WARNING, only\",r5(CCBuse[j]),\" of a district's worth of ensemble intersected the cluster geometry\")\n",
    "        # Add more stuff here to inflate the poly and try again ...\n",
    "    else:\n",
    "        CCBareaFrac[j] = HDfrac[t]\n",
    "        print(\"our final fractional capture area to normalize this cluster's usage is\",r5(CCBareaFrac[j]),\"from\",nIntersections,\"intersecting HDs\" )\n",
    "        plotPoly(MAP,0.2)\n",
    "        plotPoly(geo,0.8)\n",
    "        plotCenter(j,geo)\n",
    "        plotPoly(HDpoly[t].intersection(MAP.buffer(1)),1.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aad5e7db-93db-43d8-8196-dd6dd02f3014",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am temporarily overriding these area fractions to test our recovery from incorrect starting pts\n"
     ]
    }
   ],
   "source": [
    "#print(\"I am temporarily overriding these area fractions to test our recovery from incorrect starting pts\")\n",
    "#CCBareaFrac = [0.17, 0.25, 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "217527a2-0acf-43ec-9a25-09e0aecce76d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the main code to determine each HD's pop based on UNIT CP's that intersect the HDpoly\n",
      "We'll later add nearby or jettison faraway contiguous units until we approach the aDP\n",
      "  the HD pops and lists already appropriately include captured corner county clusters\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to enforce areaFrac threshold capture for clusters; helps to even up their usage 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on HDno 8\n",
      "working on HDno 778\n",
      "working on HDno 1560\n",
      "working on HDno 2385\n",
      "working on HDno 2899\n",
      "working on HDno 3435\n",
      "working on HDno 3968\n",
      "working on HDno 4475\n",
      "working on HDno 5004\n",
      "working on HDno 5525\n",
      "working on HDno 6071\n",
      "working on HDno 6686\n",
      "all done assigning units to HDs based on original HD polygons\n",
      "Here is a scatterplot of active HD pop excess vs relative to target 769364.2692307692\n"
     ]
    },
    {
     "data": {
      "image/png": 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3nS1Hk5GTXdQvBpbgsaauCX0WZWP573vK8kssF0qdWmxOqXfA5gIDVIhurRH0mGs3GgBYnlNYlnXyh+NFfM88HUVffROVNfWIaqWBNjRYltce6nGSMUd37+4GTYBllzlr9/m1Gw0200H9pdgda/D4a12jInpMfEmpU4utKfEO2JoYQ2Oss+L84XgR3wsMUKH6Zj3+3zc/Y9H2U5izhb3n09uox0mmWIaQhGoTqoaR40wRfL+EKLQJVePaTdeBmHmAoeQcFmtCLjD28sTILf6UKKz02Y/9EqKgi9C4nDjiCuusOKUfL+J7SlrQnnqcZMrTSsD2XLvZgKc+PGSK4AMDVPi/QfFMj+UDDKXnsFhL7hwpqL0Sekx8xd8Shd0pBiknE+7tJNpzsQS7Sj9exHeUVg+MAieZkvKu3DzImTG8G8JcTEluE6ZGv4QoxX24WeRdqBL8GCX0mPiC0hKF/WW42drOglIMXrYLb3/rvMyIEEoJdokyKW2Yn4bqZErKE5V5kDO8ewyCgwJwo77JYXv+QuiPxe70BuFBkFjvjVJrHTmipERhfxpuNie0TIkrcq2jQ/yL0ob5KXCSqeTOkQhQwWa9LDGVVtfiX7nFv82ocazqt+RwpX24Wew/84vgxwgd3rPHXy/crirdy2HflJRLIYQUeZEcgIn3dhTxGQmxxXozWlx+Q+ItYUNDdTKVd6FK0qCJt/s0W+DA94qwUEq3fpORQ3bhVcGPW/P9OY9e19/yxKzxtYQ2TRmAtyf2wqYpA7D7L8OgDQ32+bCYPw4386TIiwSAt3LOyHJmE/Efrob5eStzTsvic0iBk0x5q9dm75lypnb8UJKSclhcOVxUieraRsGP+2i/+wUx/fnCbc48Ubj6Zj2Gvvmd4OKKUhAy3Ky0HChv5UUSIjZ+mJ/lGyaH8yMFTjLlrV4blo8fHwzxH24ANsGT3HJYWLh7oam+2Yh33Ey8VVoSJCtHQYbcetdY3/PsQj0GLxNeSduXpM6L5AD85fMTuOkkH9JdSgtSifhSk2IxZ0Q3p23kcn6kHCc7Vq9ejTfffBOlpaW46667sHLlSgwZMsSr2+DJaudimz/uVjCkhBwWVp5caN7+9gxqGxqRMTZR0OP8MU/MUb7W/HE9sGj7KVlVEmd9zz/aX2zzu9LqWkzdeAyrJ/XG2J5xIm+Z57yRF3m9tgmJr+3En4YkCP7sOyJFvp+/TbyQE2fHVshxt9c2PjqcaRt8fX6kwMnKli1bMHv2bKxevRqDBg3C+++/jzFjxqCwsBCdOolXF8UVd1c7l0JkeLDF//2l2F1VjWfFAd/fU4R7OkRibE/2k7u/5Yk5S7Se9lm+08fyd48f7y9CdGuNVz5HYtyQzNiUj3ehEvS+e4O38iI5rvmzf7bsVzw3pKvD94zlIipFor6/TryQEmvA4+zYArC7PNijvdpjRKLOlMJxuKgS2YV6/Pf4FVTW1Fs8D+tEBF+fH1Ucx/m6Q0NW+vfvjz59+mDNmjWm3/Xo0QOPPPIIlixZ4vLxBoMBWq0W1dXViIiI8Hh7HH1Qx98Tiw/2FHkloHp7Yi883Ku9ze+VfFfXZOQweNkuj5Npw4MD8cOC0cz7Xd9oxIAl31qcMMzx07/3vTRc9sdSrGNozhsXOP5iDXh2Q7JWZrPvtuZfxpwtx73+uvbeM5bgxdXnx/y7AID5wm4vEONbKnXGpDOenodZ36tV357BSjspCqw39q00gQgKDHA4i5t/njZhalTfaHD4nLoIDfa//IAk50fW6zf1OJmpr69HXl4eXn75ZYvfjxo1CgcOHLD7mLq6OtTV3eq5MBgMom6Ts96d3p0ibT7wUohuZbtgqNLv6sSagVRT34Su83aY/t8mJBA7Z98PXRvbOyL+mDkLmvjp3//74YqsglF7J2cpZnF5oyQAP9y8YNuPHi1JIrfldyp/9awH1V3W7xlrLxJrvt+7u85i0+ELFu+VLkKDBePvsviM+MMi00J5eh5mea8A4KXMH1B90/5EGtabj1/rmgA4zo/j3yOeo4Dsel0jviko9elwOQVOZsrLy9HU1ISYmBiL38fExECv19t9zJIlS/D6669Lul38DCVrqUmxGN49Bn0WZePXOuGzw5hZfXr9oQ6OVGPk12qbMGDptwhVB+DUojGm37MUJgwLDoQ6KABv5dy6q5NDMGrv5BwVrkbP9lrRX8tbF7jUpFj8VHrd7h00K7kVe42yGlL3FvP3bHj3GObghfU7+FbOaZvf6Q11mLrxmEWvn9gFevmbBX31TVTW1COqlQa6CPnczDg6p/C5eNY9otY3P8mdI13O8E3/9wmnxZHFxqF5Ufk5I+7AhgNFdnunauqaMO2zfDx/6ZpoeXZCUeBkh0pl+aXgOM7md7yMjAykp6eb/m8wGNCxo/cKxh0pqpQ2aAJQbpYL5C93dVKPkd9sMKLH/K9xatEY5sKENfVNgNVJytfBqKOTc2VNA74/zVbKAmDvzge8U4G+ycjh4wPFHj+Pr5NUzem0oR4/hwqANkyNxibjbz0EbPj37F+5xczBixjfwZe/OGk614g58cLezQJPDjcz9Y1GzNta4PQ7ZX5sdvxQile/LLDo7W4dEojrtc7fY28GTeY6RYUiJCgQgOPizO7kmIqFyhGYiY6ORmBgoE3vUllZmU0vFE+j0SAiIsLix5s+PVQs+WuYn+D8ZTo9nyQspZsNRuiv1Xo0pOXL2k6eVqLm63qtntQHOjeOtZRByeGiSly76bxiPgvri78vp9X3S4hCuIt1J13hAPzfwHgczBgBB/eKTl2oZKvsXHa9lrnooTPXbjTg4LkKAOJVn3ZUQoNXalZKw977LeVnoMnI4e2cM+i9MMvhkD/v2o0GvPT5D3hjeyGmfXbMpr2roMmXKmvqmZbDmv9lgU9KV1CPk5ng4GAkJycjOzsbjz76qOn32dnZePjhh324ZfbtLCjFjgLhla+FsC5oKcfp9O4kR/KzFqf+liQslXHv7Mbfxid59Bx8MHrwXAUCfruzljL/iT+e+8/+4nbAZ17XKzUpFqOTbuXplV+vw6Ltp1w+hyc9Eq4+E2J8PvnvBv9ajmYKeat34v/tPNXca+mht3LO4JPcYiTFReDkZWE5m6evXmdq1651iMXMYU/kni/HoG7RpkDM1Wf2rZzTuFPXyu57IuRm4eUvTmLBtkKLC3ybUDWggsUQk1ifgZ0FpXg58wdcc5BrZM/nxy559Jq+EBEShBLGALyipt4nw+UUOFlJT09HWloa+vbti5SUFHzwwQcoKSnB1KlTfb1pFvgvuNTG3xNrccGR23R6uSepV91sFO1YTP/smEUviRT76WyIQgjrul7meXpNRg4f7ityWBLA04Vl7Q1L6CI0eLJfJ8RHh6Nd6xBEhXmeDzT+nlhkF+qdHi+hQ63uzpCqbzRi3d4iwfvgSGVNAyprhPXIaQJVOHjedU+z+c1YalIsnhsSj3V7i93ZzN80H5/AABXG3xOL9/e4Pg6zNh/HyQXNC5ybY+0d5nNxrIeS7PViijHcvrOgVPKbPLkw1Dbik9wLzO19MVxOgZOVCRMmoKKiAgsXLkRpaSmSkpKwY8cOdO7c2debZkGqdamsbTtRir+m9jCdvF3VwfHmauqeJKl7K/BUB6qY74RdsT4pm++nkLpaji7OLAnsrOaP6+Hw2DurUeZpBfolOwrtXjj1hjqLpHsx+um2HL3ksiSIkLw/T24C/pVb7JUaTs7UNbFtwMR7O5mOw86CUg+DJlgE5NtOsFV2r2s04q7XduLNR+9G3qVrKK64gfi2YbinQxuPtsUeT3M/m4wc0v99QvTt8he+qOlEgZMd06ZNw7Rp03y9GU55K8rmh4gGdYsGIO1FTwhPk9QPnq/wSuD5wpCuCAxQYf64Hi4LQgrF7/vLmT/YDBk4uuC6U+VbKBWARdtPYXRSrMPPgRQV6Hf8cIWptwEQp6Cso3o09l7LVbK7pzNViypqGLfa9+KjwwA0f4df/uKkR8+lCQrAgC7Nx1TozWRDE4fZn/9g+v/eM8C/UOLR9jjiyYSHd7497bMkbbkLDw70ydqolByuUN6Msqd/ZrmmGH/Rs0741WlDvDb7y5Mk9Z0FpZj+qXe6vac90Lz2UmS4bS0ssVy72WiTSGlvLThn68ZN+yxftECSdYJAalIs9r00HJumDMDbE3th05QB2PfScLc+P01GDq9+WeDmFnuHo5sdVzcBHICXM09i/9lyx4mwCqpjzJ+73t11hjn4dKSu0YhvCpon87AkE/ua0BveJiOHD0UcgvU3jma7S416nBTKG+tS8a7dbLC4620yctCGBuOvo+/0WX0Td5PUxRyOcmXKkARTDoW3x+Gte93w27+d1WwR25YjJdAbap1+NhzVKBPqcFGl4Jwcb3N0s8PSU3LtZgOe+vCQw57EiBDf1HASKio82JRQ/9F+cQKC+V8WYHSSzmcFQIXYe7ocD/aMYz5PHi6qFCXh31/9WtdIyeGEnbfWpeJxaL7wGo3Aou328zBYTwZiLNXiTpK6p9PrhRiZ2A6vjLtVnM0X4/DWPT/eGJo099/jV/Df41cA3PqMSLXGoZzqKVlzlfcnZNsdDd2pFDJ28IcBnREYoELuuQqHlaiF4mdW+aoAqBCfH7uEfWfL8bcHeyAyXOPyeyDnz7VcUHI4YeaLbunS6lpM+8x2iMv6ZO4sMBJrFhxrknpy50jknqswTYOXOngIVAFvT+iFB63W9hNjcVl3yeHky1czbqUJsijYqg0JwsjEGAzqdptHvZa+XvTTEZa8PyHb7ih/r02o2rMN9ZKE28IBiP+Z1FffFKUAqDfoDbYLYDs6B0ZLOMTvLyg5nDDZWVCKRf/70debYWJ+MnfWIwVAtKVaWJLUx98Ti6Fvfid5sJQQHYb2bUIxZUgXDO52m8MhKUfbK7XocA1+0ou7hqK7rKvcV9c24vNjl/H5scsAgMgwNd54JMnpOlT2AvN+CVFMlZC9jSXZXWhQbS/R2N56knLEX+SKy8VNZq+sqcf4Xu1Fmb3qCw7PgfJdfEE2qlwUApWCiuMUlFWoAKyrK7trxw+ldnt95Mx81WtnK2PzK6F7urJ3VLgavTu2wbc//eLZhjN6e2IvPGzVw+SIWHWSWPDLZ4QEBSoicdbc8/cl2F2HylmP5aHzFdhwgL3+i1RahwQiuVMkhnS7DWkp8Ta1guzhc+8A9qDa/HOXe64CT6476O4me0WbMDXyXh2J7EK96DWJ3prQC4/2bo8dP1wRffaqN8VanQO35l/GnC3HfbtRMmd9zDzBev1WyMg4AZqnW8/YpKygCbh1IXA2g8bdpVrMZ2b9cVA8osKDUVnT4LWgCRDWVWy+vW89cY+keRl8kT6lBU1A8zpU//stP4rnbFbgCxuPoY0IRS09ERYciKjwYFyvbcL3p8uxaPspDH3zO4uZjY7wM1VjItg/S+afO28sIeSp/xuYAACS1E/T/XbcpJy9ai2EISAWqrS6FgfOlJuWbDl2Qd5LV8mBL5b4oqE6hdhZUKroOylW7uQ+BAaoUH2zHhv2F3s9fyhA1TzDUQjzmWQ/6Q3MtYeECg8OVPSMnD9vyUdAgApje8Yy1e3adLgEuogQnwWKN+qbbOrtCB+Gdv0JtpdsLqRqti9oQ4MwY/jtkhXuLf9tRp038/mG3nkbvvlR/CWv/rDhsNfPY0rn7TxO6nFSAG9VuXZmypB46CKkv5tzJ9HPm7PlrBm55hmO7hBS6dgdSg6agObSRNN+qyHGUrdLb6jDk/06eW8DGbAu0sz3pukNzqfUO0o2l/qz5Km6BiMWby/E/rPlkjz/a18WYP/ZcpzWs62VJ4YDZyskeV4KmoTzdoI49TgpgLeWV3FEGxqEl8ckopVGbbFshZg8WarF18fH3bsdX2+3Urz+VSH+mtqdqW31zXqEawJRUyefoNFZ1egmI4eD5yrwcuZJpgumo2RzuX+WahuNWL+/WLLnr7zRXOfKm67XiVNOgXjGeiF6b6DASQF8PZ28+mZzkbH46HBJX8fdpVp8fXzcvdvRV98UeUv8U2l1LXNxw48kvDh7yl4xVqETBRytAZhTqPd4+whRIvO1D72FhuoUQA41avip31KZPeIOt5dq8eVUbE/udip9MI1WqaLCg10mP3v53CmY+ffHUaK7K4u2n0J9o9GUPJx7rgL1jUZkHrsk9uYSogj82ofeRD1OCuDL4ok8vl6OVNvh7od/Z0EpFmzzXU0rT+52ohRSe0cOdNpQl8nP3qykby5cHYAbDUaX3wm+3ownOXml1bUYsORbi6A7KlyNayJV4SZEaXzRsUA9TgrAF0/0BRVu9apIuR3ufPhZE2ql1Kmt+3c7OgFTz1uyqHA17m6vxX/yLvt6U+yqYQiagObCsHzxTk/ykax7KuW+Rh8hUmkTpvZ6fhNAgZNi8HVeIsO8t7SCvRk8/HZEhYu3He4Md/lyJp05TxYWTe4cSYWBGVTWNODuBd8ofmiTTxD3dU4eIf7i2o0GZPsgv48CJwVJTYrFOxN7e+31VCrgT/cl2OQepSbF4mDGCLTSiDPSO/6eWMHDXXKZReROAcsmI4f9Z8rx189P+DzwUwp/OU5l12sR5eNCnYT4C36pL2elPqRAOU4KU3nDe3fdRg74YE8ReneKNAVP/FCDmDPC/pN3CXNHdWdamoInl7t2oQuL7iwoxctfnHRaRZ34r3/mFuPYhWu+3gxC/IKzUh9SosBJYbydCMcBmLf1JIZ3j8Gun65Kss5aZU0Dkv+ejWWP3e10cVdzcphp2DY8WNAQ486CUtHX6CLKkkdBEyGi8/aNNAVOCsPPbPPmMFVlTQP6LMq2WdleTNdrGzHts3w8f+ma3cVdrfVLiEJUuNqnibHje8UxDzE2GTmfzv4jhBB/Fe3FNQoBynFSHH5NKm+TMmgy9/6eIuz4wfXSEYEBKjz628rwvtKhDfsw3eGiSp/O/iOEEH/V2GT06utR4KQwcl+TSgzzvyxgSvYbkajzwtY4JiQxXC45WYQQ4m+2HvduqRIKnBRGLrPJpFRRU4/DRZUu2/VLiEIbL5ZnsCYkMdyX1c0JIcSf/Vrr3VIlFDgpTEvpuWDZz+xCvc9mp0UKLbzmL/PpCSFEblTeDWUocFKQJiOH8ustI0/G1aw5Xydb1zUKG1Mvr2kZ7xshhHibt1dhoMBJIXYWlGLwsl1YtP2UrzdFcizT/H2dbH2jvgnv7jrL3F4O5RMIIcQfJUSHe/X1KHBSAHdXUleqRQ8nuZzmL4chyw37i5gr1uac8u+EfkII8QUVgLSUeK++JgVOMieXNdm8pXdHLcb2dF1uQQ49ONduNjAlsdc3GrF+3wUvbBEhhLQs/ROiBK06IQYKnGSuJcyiM8c6Uy25c6TEW8KGpedrw/4iL2wJIYS0PNPvv93rr0mBk8zJYUjKmw4VVTINf+VdqPLC1rjGUmbAF6t3E0JIS1B507ulCAAKnGRPDkNS3lTJWMNJNgFlSxlDJYQQGfLFNdKngVN8fDxUKpXFz8svv2zRpqSkBA899BDCw8MRHR2NmTNnor7eMsI8efIkhg4ditDQULRv3x4LFy4Ex1le0Xbv3o3k5GSEhISgS5cuWLt2rc32ZGZmIjExERqNBomJidi6dav4Oy2Qr4s8+gJLUCSXgJKlzMAoH1c4J4QQf1X+q/dnV/u8x2nhwoUoLS01/bz66qumvzU1NWHcuHGoqanBvn37sHnzZmRmZmLu3LmmNgaDASNHjkRcXByOHDmCVatWYfny5VixYoWpTVFREcaOHYshQ4YgPz8f8+bNw8yZM5GZmWlqk5ubiwkTJiAtLQ0nTpxAWloannjiCRw6dMg7B4KYsCzY2C8hCpGhvl+jmiWA8/aMD0IIaSkyvjjJPLtZLD6/8rRu3Ro6nf078qysLBQWFuLixYuIi4sDAPzjH//A5MmT8cYbbyAiIgKffvopamtr8fHHH0Oj0SApKQmnT5/GihUrkJ6eDpVKhbVr16JTp05YuXIlAKBHjx44evQoli9fjscffxwAsHLlSowcORIZGRkAgIyMDOzevRsrV67Epk2bpD8QDhwuqvRZdWyfcV6JAEDzIr+P9emA9fuLJd8cZ6pqXI+vH5NJPhYhhPibX+sacfB8BQbdHu211/R5j9OyZcvQtm1b9OrVC2+88YbFMFxubi6SkpJMQRMAjB49GnV1dcjLyzO1GTp0KDQajUWbK1euoLi42NRm1KhRFq87evRoHD16FA0NDU7bHDhwQNT9FUpvkEkujxexdr36epFfAFi0vdDl3U7u+XIvbQ0hhLQ8uecqvPp6Pu1xmjVrFvr06YPIyEgcPnwYGRkZKCoqwocffggA0Ov1iImJsXhMZGQkgoODodfrTW3i4+Mt2vCP0ev1SEhIsPs8MTExaGxsRHl5OWJjYx224V/Hkbq6OtTV3brQGwwG9gPAoNIH47e+FhUWzNSuX0IUIsPUqPJhj1xpdS0OF1UipWtbJ60YutAIIYS4ybtDdaL3OC1YsMAm4dv65+jRowCAOXPmYOjQoejZsyeee+45rF27FuvXr0dFxa3oUaWyvehwHGfxe+s2fGK4GG3svb65JUuWQKvVmn46duzotL1QbUJbVmI4ABSWVjO1CwxQ4ekBnSTeGtdcJbM7D6oIIYR4on+Cd8+xovc4zZgxAxMnTnTaxrqHiDdgwAAAwNmzZ9G2bVvodDqb5Oyqqio0NDSYeod0Op1Nr1BZWRkAuGwTFBSEtm3bOm1j3QtlLSMjA+np6ab/GwwGUYOnazdbWH4TgLwL15jbcjIoB+AqQfzeeOfr7hFCCPGAl68DogdO0dHRiI52L0krPz8fABAb27zkRkpKCt544w2UlpaafpeVlQWNRoPk5GRTm3nz5qG+vh7BwcGmNnFxcaYALSUlBV999ZXFa2VlZaFv375Qq9WmNtnZ2ZgzZ45Fm4EDBzrdZo1GY5FfJbYohgKL/iZMzd4Reu6XGgm3xDkVAJ02xOWCxHIp1kkIIf7oYFEFhtx5m9dez2fJ4bm5uXjrrbdw/PhxFBUV4d///jeef/55jB8/Hp06NQ+/jBo1ComJiUhLS0N+fj6+/fZbvPjii5gyZQoiIiIAAJMmTYJGo8HkyZNRUFCArVu3YvHixaYZdQAwdepUXLhwAenp6Th16hQ++ugjrF+/Hi+++KJpe2bNmoWsrCwsW7YMP/30E5YtW4acnBzMnj3b68fGnC5CHvWKvCkxTsvUrsnI4eA53yZev/ZQoiIWJCaEEH915dpNr76ezwInjUaDLVu24P7770diYiL+9re/YcqUKRZT/wMDA7F9+3aEhIRg0KBBeOKJJ/DII49g+fLlpjZarRbZ2dm4dOkS+vbti2nTpiE9Pd1i+CwhIQE7duzA999/j169emHRokV45513TKUIAGDgwIHYvHkzNmzYgJ49e+Ljjz/Gli1b0L9/f+8cEAf6JUQhVtuygqfbGIPFw0WVqLrZKPHWODauZyxSk5SxIDEhhPiruEi2NU7FouKsS2wTjxgMBmi1WlRXV5t6xTy1s6AUL2w81mJW9/j0uf5MNTm+PH4ZszYfl36DHGgTpkbeqyNd9jg1GTnc+0Y2KmtaXr4aIYRI7ZNn7sXQHu08fh7W67fP6zgR11KTYrHm6T7QRbSMfCdjE1uI6OuenGs3GpjW1QsMUOHhe+JctiOEECLc6V9+9errUeCkEKlJsdj/8gOYM+IOX2+K5A4WsRUz65cQhfBg336E9dVsY+sdIsMk3hJCCGmZSipvePX1KHBSkMAAFWaN6IY5I7r5bBv6do7E2xN7Yf64HpK9xpFi1704QPPxGOzFMvv2sFY5jwpnK+pJCCFEKIUXwCTSi48O99lrj74rBg/3ao/JgxLQOiRQktcoLDUwL9oYGuzb5RZZ62zptN5NXiSEkJaiV4c2Xn09CpwUyJe5PdGtml87MECFR3pJk7fza10TU+4QALRv49uARMW4nEq/hCjJAk1CCGnJ4rycCkGBk4I0GTnknquAvvomosKDfbIC2mvbCky9QRcqpKudwVr7aKCPh+pYl1MJDFAhuVOkxFtDCCEtT1VNvVdfz7fjHITZzoJSvP5VIUqrfVtM0VDbiMNFleiXEIUjxdKtSM3aq/b9z1cl2wZXwoMDMaAL+xpJ0S2wCjwhhEht0fZCjE7SuSwNIxbqcVIAvo6Tr4MmXtn1WhwuqsTNBmkS8mIZljEBgB0/XMG6vcWSbAOL4CD2r8/OglJ8fuyyhFtDCCEtU2l1LXN6hxiox0nmmowcFmwrlFXxy+9/KsPNBqNkzz9/nOtlTJqMHF79skCybWBR9VsdJ1fDdU1GDq9/VeilrSKEkJbHm0tbUeAkc+/uOgO9QR49Tbytx69I+vyRDFP3DxdVyqISN8t7c7ioUja9hYQQ4o+8OWmKAicZ21lQirdyzvh6M7yO5c5BLgvnVjLUcZLLthJCiL9RAdAxpneIhXKcZKolD++w3Dn4erkVHkthS7lsKyGE+KPXHnKd3iEmCpxkqqUO77AmhvdLiEJUuNoLW+QcS2HLfglRCNdQDSdCCBFT2/BgrHm6D1KTYr36uhQ4yVRLHd5hvXMIDFDh0V7tvbBFjrEGeQAQ6IuiW4QQ4qeiwtXIzXjA60ETQIGTbNHwjmsjEnU+fX2W2X9Ac++hobbJC1tECCEtwzMp8V4dnjNHgZNM9UuIQqy25QVPr39VyLxOXXLnSPjoewOAbfYf0HJ7DwkhRCpv5ZzB4GW7sLOg1OuvTYGTTAUGqPDaQ4m+3gyvE1LILO9CFRhjLEmwBkTUe0gIIeLTV9fihY3HvB48UeBEZIe1blVOoV7iLXGuuPwGU7t+CVEIV9NXjRBCxMTfNwsZqRADnc1lSm7lCJ4dHI9Pn+2PMUkxkr8WS22kJiOHrcd9u4TJ5iMlTF/WwAAVnruvixe2iBBCWhYO3l9yhQInmZJbOYIdJ/UY0LUt1jzdF2uf7oM2odLVTmWpjSSHyuFCvqwzH7gDGgFr2xFCCGHnzVxSOpPLlNwSis2DhNSkWEy7/3bJXoulNpJcjg/rdgQGqDCpX0e3XiMprrVbjyNESq2oNhmREW/mklLgJFNifwjEmHxmHiQcOFcuwjPaYq2NJJeEayHb0SEyTPDzv/PEPSj/tV7w4wiR2q91VGKDyIOQmnpioMBJpsQuRyBG2lxUWPMQWpORQ15JlQjPaEkF9gKYvi7XoILwL2tUK43g11iwvRB6g+ucL0IIaaloyRUC4FY5AjkVnP5JbwDQnF90XeSCjrHaEEGl831ZroF/T4R+WXURwgI9DvB5HhchhMhVgApYPYmWXCFmUpNisebpPrIphHmx6iYA9nIBrFLv0mH57+/BSIGVwEcm6hAe7P08C53AII/XLyEKughhvU6EEELs+/Pwbhjb0/tLrkg3NYqIIjUpFsO7x+BfucUoqqiBCkBrjRqrd5/z+rZ0jgrDzoJSLPrfj6I+784f9dj5ox6x2hC89lAic0By8HwFauq9m2cxf1wPTB6U4Fa3cGCACgvG34WpG49JsGWEKFufjlocu1jt680gCtLltnCfvC71OMnczoJSDH3zOyzafgobD5bgXwdL8NnhEq9vhwpAu4gQvLDxmGTDR0KrwOaeq5BkOxxpE6Z2O2jipSbFNpdzCFOLuGWEeNfoxHai954eu1gtq9QEf/d/AztDG6LsvhNfTRKiwEnGdhaU4oWNx2zqOV27KV7gwjrUFRykwhvbT4mSZO6I8Cqw3l1v5dqNBry764zHFWpTk2KR9+pIzH6gm1fX2gsJCqALExFF3/i2+NuD4uYYqgAEKuCKFOonqwCM6KHD6qeTfb0ZbtNFaLw6k86cf3wC/BBfOdyHS7FZqGvkRM9tskdIFdiULtGSb4+1t3LOYNBSzxeWzC7UY+W3Z7y61l7Qb1clCp6Ip6JbBeNM2a+iPicHoNEo6lOKLlYbgmPzR6GVRtk9NQAAFVB2Xbkzdmsbjcj20bJbFDjJlLcqh3s7R4gVS2HJAV3bItwHRfj0BvcXlmwycth/phzp/z4hwZY592tdI2aPuANaGiYkHmrXOgQb9hdL8tzqQPmG9vPHJSI4KAAcJ5dbWvflFOrx6n9P+nozXApxsOJC9Y0GnyzwC1DgJFtyqYztKyxj14EBKkzs6141bjEIXVhyZ0EpBi7JwVPrD+GGjwLW6pv1qL5BJQ5aGl2EBrqIEFF6G2O1IYBK3JQBc6PvEja71psiw4Px7q4zsr3hFGLDgQuoUUAR0zAHvXu+WuAXkDhweuONNzBw4ECEhYWhTZs2dtuUlJTgoYceQnh4OKKjozFz5kzU11tWSj558iSGDh2K0NBQtG/fHgsXLrSJ+Hfv3o3k5GSEhISgS5cuWLt2rc1rZWZmIjExERqNBomJidi6datNm9WrVyMhIQEhISFITk7G3r173T8AHpBLZWxvE1pYMiLUN70nQheW3FlQiqkbj+Hqdd9WAf/0UIlshn+J9zzZrzMWjG/OSfI0eEpqH4FyhoW4hVKheQLG9h+834PASl99U7SeNuq1cE4FICpcjcoax+dMXyzwC0j83tXX1+P3v/89XnjhBbt/b2pqwrhx41BTU4N9+/Zh8+bNyMzMxNy5c01tDAYDRo4cibi4OBw5cgSrVq3C8uXLsWLFClOboqIijB07FkOGDEF+fj7mzZuHmTNnIjMz09QmNzcXEyZMQFpaGk6cOIG0tDQ88cQTOHTokKnNli1bMHv2bLzyyivIz8/HkCFDMGbMGJSUeH8WG18ZW76d1tLgwF5YssnI4dODxZJvkzMsPYNNRs4nQ3P21EmURCJFPS2aeSie+OgwU104nVVdOJXAk8y3p8pMqwiIRYVbPQhyDuwra+pF62mTeTqXz3EAHu3Vnqmtt0doJM1we/311wEAH3/8sd2/Z2VlobCwEBcvXkRcXBwA4B//+AcmT56MN954AxEREfj0009RW1uLjz/+GBqNBklJSTh9+jRWrFiB9PR0qFQqrF27Fp06dcLKlSsBAD169MDRo0exfPlyPP744wCAlStXYuTIkcjIyAAAZGRkYPfu3Vi5ciU2bdoEAFixYgWeffZZPPfcc6bHfPPNN1izZg2WLFki1WGyi6+M/UILq/kz9I7bmOs4HS6qRNmvvh12YukZPHCm3GdDc94i9tDFmCQd7oxpjZXfnhH1eVsq/nOamhSLkYm65u/O9Vq0ax2C5M6RyLtQhbLrtThaXIl/HXR+o2jkmlcRiNWGiJaHqdOGYOK9HfFWjjzfbxWAmAgNKm/QupHexDqi4O0RGp/2Fubm5iIpKckUNAHA6NGjUVdXh7y8PFOboUOHQqPRWLS5cuUKiouLTW1GjRpl8dyjR4/G0aNH0dDQ4LTNgQMHADT3juXl5dm0GTVqlKmNPXV1dTAYDBY/YklNisV7k3p7dcq6rw2+vS1zWznkgVU56UbmZeZf8sKW+JevC/QUNInA3tB3YIAKKV3b4uFe7ZHStS2CgwJM/1cxdj9drLopypJH2lA15ozohn0vDUd8tG+KGbrC94bVNhrx3nfeLzzcUqkAbDpc4jQ3z501Q8Xg08BJr9cjJibG4neRkZEIDg6GXq932Ib/v6s2jY2NKC8vd9qGf47y8nI0NTU5bWPPkiVLoNVqTT8dO4qbrBwZrvHqlHVfW7+vmHmWhNh3GfPH9cB93dgDNwBYtN11YqK/9zYRebK3pmKTkUPuuQp8efwycs9V2Hx2O0eFMT1356gwUYq5Gm42YGXOGWQX6mWR1zllSILNElf8LNRrNKnCqzgAekMdnuzXCYBtbp67a4aKQXDgtGDBAqhUKqc/R48eZX4+e3c4HMdZ/N66DZ8YLkYb69+xtDGXkZGB6upq08/FixcdtnWHHHpVvEnIVP9+CVGICBEntyYqXI2oVhoMvaOdoMexJCbeG+/e3VCbMDXCfLAWn1AqwCdlIYhz1msq7iwoxeBlu/DkuoOYtfk4nlx3EIOXWdYkS0uJZ+rhbhdxa+gv79WRmDPiDrSxGlaJ1Ybg+fsSnFYYN58Zldw50ufrcv7vh1Ls/sswbJoyAG9P7IVPn+2PkCDlf7ZlXOHBpfjoMLu5ee6uGSoGwTlOM2bMwMSJE522iY+PZ3ounU5nkZwNAFVVVWhoaDD1/Oh0Opsen7KyMgBw2SYoKAht27Z12oZ/jujoaAQGBjptY49Go7EYRhSbHO7CfOH1rwoxMlHn9E4iu1APQ604vTmVNQ2Ys+W4W491Fdw+MzAei78+BdbSL4Eq4NHe7bH4sZ44UlyJpz485PpBPsK/O1MGJ2Dlt2d9sg1twtQICQr0SoFWpWgVHIjdfxmG4N9q4PCrEFh/BPlljvgLUHBQAJ4dHI91e4udPv/iHacw9u5YBAaoEBigwqwR3TBj+O0WuVP9EqIQGKDCfXe0c/oZ5mdG5V2oMuV1+qqTnd+OlK7N143ccxV+8blqUvCoRbvWIUjp2tYmN4//fPmC4B6n6OhodO/e3elPSAjbxT4lJQUFBQUoLb11x5OVlQWNRoPk5GRTmz179liUKMjKykJcXJwpQEtJSUF2drbFc2dlZaFv375Qq9VO2wwcOBAAEBwcjOTkZJs22dnZpja+0C8hqsXNLmKZYspXVpcDV8FtcFAA/jQkgfn5mjjg82OX0W9xDqpv1Mt6diV/1/fnB+5w+TltpQnEWxOa7+LFqikENA+h/OP39+DTZ/vj4Xu8f/cpR7/WN+G975oDWWerENirhTO8u+s6Sva+n9a5U/xFjbV0Qdn1WqQmxWLVxF5M7aVifiPU0nr85cQ6f8nR58sXJM1xKikpwfHjx1FSUoKmpiYcP34cx48fx6+/NpfqHzVqFBITE5GWlob8/Hx8++23ePHFFzFlyhREREQAACZNmgSNRoPJkyejoKAAW7duxeLFi00z6gBg6tSpuHDhAtLT03Hq1Cl89NFHWL9+PV588UXTtsyaNQtZWVlYtmwZfvrpJyxbtgw5OTmYPXu2qU16ejo+/PBDfPTRRzh16hTmzJmDkpISTJ06VcrD5FK9D9cheLxPnOtGEnF20vJWZXVXWBMTM8YmIqVLpKDnvnajAdM/y8f434IBuQVP88f1wL6XhiM1qbnnYULfDk7bP9W/Ex7t3R6DukWbagqJ5dufruLFz0/gyxPyrQHkbW9/ewZLdhS6/K5Y36iwBgtfF5Ra5Ek5yp9i7TXn21318TIg5tvbUnv8HeFvjqQ+F/kyf4mFpOUI/va3v+GTTz4x/b93794AgO+++w73338/AgMDsX37dkybNg2DBg1CaGgoJk2ahOXLl5seo9VqkZ2djenTp6Nv376IjIxEeno60tPTTW0SEhKwY8cOzJkzB++99x7i4uLwzjvvmEoRAMDAgQOxefNmvPrqq5g/fz66du2KLVu2oH///qY2EyZMQEVFBRYuXIjS0lIkJSVhx44d6Ny5s5SHyamD5yskTy7u2SECv1yvt3tyzTx2BSoVmIeZxOTspCWXO8H549i/2J/8cQDufPVrQcMQHIBtJ0rx3qQ+WPi/H6E3yGdtqejWGouk420ugpb/5F1C91gtdBEhGJmow5qn+2De1pOorPE86fYjiZb/8KU2oWrEajU4pXd/Tbj39xShvpHtE8d/p1iDhX/mXsA/cy8gVhuC8ffEYtuJUotzSKw2BK89lIiRiTrEakOgr661+9lXobnnkr8BuVB5g+n1pdAmTG1xI8TX03O07S3Fs4PiMSJRh34JUcgu1OP1rwpd3rg6+lyw0Jl9dnLPVchieM6civOHRXdkxGAwQKvVorq62tRr5okXNubh6wLpFjJ854leGN+nPZqMHN7ddRZv5ZyW7LWEiAoPxpFXRjj8kuw/U46n1vs+92fTlAGmfAgWf/4sD1/9IPz93DRlABqbjEj76LDgx0rl02f7Y1C35oWWc89V4Ml1B5kfy19UbzYY3c4t4wWo4HczT6PC1Vj40F145b8FqK5t9Oi5IkKCYGB4Dv6z3GTkMHjZLo+DBf6bu+bpPgBgqknHOWjDJ/mu33sei7af8uCVbUWGBaHqhutjMPuBbpg98g6L3/H5YYC8i3NKgf+emidgNxk5rPr2jNNyIWOSdHh6QGcM6NIW3xTo8eqXBU4rgFvTRWjwcK84h8G4VAnhrNdvqvouY01GDntO/yLpaxSZ3d1tPuL9CumO9E+IdH5n4fubDgDCe77+8URvtza97HotDnl5WQFXDhVVmP4t9DjwScklFTVuvz5/HP0taAKAqpoGzNh83OOgCQAMtY2ICg9mroXDF9/l/+Yu8/wpvoeRZWYU68w+IdL6xzO1u9fOsLujiuu+JPXpb/r9XbFpygDTUDxvZ0EpBi391mWNta8L9Hjqw0NI/ns2pn12TFDQBDSXIXh/T5FNTxV/3vDFwr7mKHCSscNFlZIvJvlWzmnsLChlzhlSe+kT0/W21k7/LsZaWWKcfITmQORdqHLrrrX5deQVIXy0v1hwHguP3xNXBe6c0WlD8OygeDce6VjagE6YPNB3Q/M8sd/pR3o15yqy1sIRK1gwz59KTYrFvpeGm6b627swA82TKR7oIawsiCusWaKOziupSbGYN7aHeBvkoT8OjpfsuduEqZE+6k6bBGy+501IuoDYta98ubCvOQqcZMxbeTyvf1XIPOW2wUt56q6Gv8RI2vT0a+dOxVqh76l5b0BKl2hBj5Xar3WNpoRid9ZWdFXgzpX543pgRKLrGWBCBAWoMLKHuM8pB0J6fHjmgc4fUjwLJvnPvbOZUXxy+dZjl5B34ZpHr2eL7dvu6Lyy44crmLkpX7St4ctEuOP5+xIw/8G7MOxOac4Hix9JsuntdzYz09t8tbCvOUmTw4lnvDWjo7S6FvvPlHvltVhVu7hTSe4c6fPcFndmfLjznvKvM6BrW7QJU8uqgrH5BZGvwWO+YCsLvsCddcKps/dXBWDR9lPY/ZdhoibvbjhwAVf9oG6POT7wDgxQCa6Fwwc6QHMyuLuKy50nfO8sKGVKOHZXSpdoZB67zJygbr1t0z4TL2gCgPE9Y/H5scsO/z5lSAIyj122GOKKClfj7w8nYWzP5t7Dwbffhu9+du+8PSYpBgfOVtgdCl60/RQCAlQWwbRcZjGb8+UEIepxkjF37uLdlVUoXQK6O1wtZZJ3ocqnQVMrTRBGutHb0S8hirkaeJswtUVvQGCACksfu1vwa0rJPBB0d3inXesQm2Gc+eN6OH1/rYsmikUFYLfEeYXepIJlgO9uLRxPz0Wbj5Q4/D7zQ0BSXZhjtSEY0LWtw7wtZ1Pfm4wcXv7ipKjbE6CCw6ApPDgAvTu2cRk0AcClKvdnH/btHIXH+rS3+zd7eURyLALqy1IRFDjJmFhJmixYZt2IYUxSDCIZCnq66or1dTkC82EqoRjXUcV7T9oOofDrgzlbxsJb2oYH29yh8wHQjGG3Mz2H+fRv84t6dGu2/eOLJq4R6ZhwAGrqfVc3TWyP92kvygwkT89Fjr7P3hgCGn9Pc50xR4F9ZLga703qbfc4vbvrjOg9vM5uCGrqjci/eM0mmbqqprmmGx/M7CwoxYYD7vUABqiae5UcPd5eHlGlCDmlYvHVwr7mKHCSOUdfdm2oMkdZvy64itoGtoR3Z8GRHArTuRO8HS6qRE2d6/1vGx6MAQ7yvFKTYrH/5QewacoAU9KvLzzcK85uj0VggAqDbmfLv/i/gQl2n0No0UT+mPyuj/MinC1NmEa884Sjc5H1GnWO2Pu+eGMI6P09RaaAIzUpFvPHJSIqPNj098qaBizafspmplaTkcMGEeuDsd4w2WMezNQ3Gj1aNYGlp946j8j8ePmSXApjKvPq28KkJsVa5CYUl9/wSb2lUHUAboqQHc76HM4unlIWpmOte+NO8KavvsnUjr9LdoTvnfnazWm5rTSB+JUhgHPG2VAly/vTJkyNGcPt90y5ery9nJTAABWW/a4ncn66Kqs8MF/qHBUm6vNZn4vatQ6BkeOY1lO0931xZ7IEByA8OFDQjOOML05iZKIO2YV6TP/M9Zp9QHNQd+2meJ8jTysm8sHMv3KLvZZvxL8/Om2oV17PFZ3EdZxYUY+TQvAXygd7xolWbykqXNgaeE/07SjK67rC0hXLDx1I0cU/eWC80ynynnQVs9Yz6RDJdsHr0Ma9E9qUIV2hgvtDwPaG6cyxDO0sfexuh8Ghs8c7u+vk88A8uRcNC/aP06IKzTWRxGadJ1V9w/Vn2tH3RejNh04bgtWTekMtcFZa1Y0GHDhbLmjNPl+nAzhiXj9Navz70y8hiinFQirPDop3WL7CF/zjDNGCeNK1rYvQYM6IbqYaKq+OFZZU+9UP0hcdE9IVOzJRJ8kCyO/sOovaxiZwEHbRZhHVii0Ph7Wd0MCRD/peuL8rZo+4A1rGYRZrrnrEAMdDO7HaEKx1MAWe5fHOptCbPy7WQZK6q/W2bvhJjhMHYNdPVyV9jSYjx1Tl29HSREKTzjmOw5myX93qUcw8dknQmn1ySAewJ6uwTPLXsFcU1dOSFJ5sy44CvWyWWwFoqE5x3L0LigpX428PJlrMyli/97yg5xBa/dUdQrpiDxdVSjYkw5dD0FpN//e0q1gXwXYyZm13UcDMGv6UM/6eWAx98zuLi4gmSIU6xjXNgOYesSYj53Jqu72hHSEnQHcfb/44ffVNVNbUI6qVBrqIEEHrbSmZCreqdkt1wWG9kYt0kCMjtIzFVUMd3spxXrXakRv1bBNg+HNsS12nztHNYZfbWvlke8wDWiHLW0mJAieFcfcuiJ+VscasPodcEv54UeFqzB/XgzkokbIrne9tCgkKwKfP9Uf5r3WiLDLJn4ydXWxYhgH5oOV8GfsCsJHhavTu2Abv7ymy+ZuQoAkA9p4px4f7ipjWkTKvBeQOdx5vHtTptKEY36u9TWXs4d1jMGDJt165IfAFb1xwWL+DztrxPYQsgawnAcy98W2Zemv4c6x5UNeStAlTY8ljd9t8j4vL3V8eSQxyGjqlwElh3L0L4gMB8ztQuXVF2wvunJF6+/nK1gEqFR7uZb/miVDWJ2N7C566GgZ0t1hgZU0Dvv1JnBpF39updWQvwVYoll4sV+wdH3tBXd6FKr8NmsxJecFh/Q5Gh2ucrnJv3kO4/+wvePe7c6JuZ5swNZ4ZGI+P9hcJmnDAB3Xztp5EZY04vdu+LtzriiYowGbiR5ORw6bDvl3LVE7XKwqcFMaTuyCbO1B5DBeb2AvunOmXEAVtaBCqb0pbg0rsC4+jO+yYCA2e7NcJdY1G5J6rsBs08MUC5XjeFfr+WWMNeFw9h73jYy+oY31fZwzrim4xrXHm6q9497uzTI9h8X8DO7tdi0cIKS84LLMftWFqzP3PCYsiivbeV75nUYpAb+ljdyM4KMDhsKCzm5bUpFjcbDBizpbjomyLkWteLii6tQan9dfx3veeBYn857Nd6xBU1dRj3taTHs0G1BvqbHopDxdVClqjTkzOqrr7CiWHK5Cr5FdX+BNT2XX5FDXjCVmHKLtQL3nQBEhz4bGulD1nxB0AVHgr5wxmbT6OJ9cdxOBluyxqy8hpvShH3F1HylH1aCGroTs7PvZmTbG+r+rAQDzcqz1zbSpX+OT4UXdJOzvIG4UCXc1+5NC80Kt15Wln76uYQ0K6CI3FRAR3Jxyw5hyOSoxhahfdWgNNUAA2iTBDmv98pnRti7E9Y/HeU308fk7rsim+GiaTS90maxQ4KVRqUiwe7OneiZe/YLBWg20dEuR25xT/uAd7xjIXygNcf1H5i6SUpL7w8HfYmqAArMw57fLiIsf1ohwRcqIVGvA44ur4WAd1/RKimKqNf3ygCE1GztS74qnlv7sHqUmxbi1jEqZmW66H540LjqNgJCZC43DWq6P3dWdBqdvJ39bmj+uB/S8/YLf6vvlNi/U0d36x4S+PX0buuQqL995ViZJnBsYzbVtx+Q28sPGYKMN/mw5bLmczoEtbj5fqsh7CdufmMVYbgufvS7D5zrQJU9t8Lhy1dRXQ+goN1SlUfaMR6/fZJvmyqPrtS8GaHP67Pu3xsZtDCpG/rbE0OikWB86UY8rGo6hlKIDp6osqdRDhrTsdV0GD+dCXnJIjXRFyohUS8DhLchaaqBwYoMLEezti5bfOh9+qbjTg4PkKDLo9Gq89lIipHiYLl9fUmV6fdUYZ/wl8fmgXpsCibXgw3ng0yWsXHHcKY1q/r0JuhtqEqVF9o8FprtLkQfar0gOOJxw4Gy52liLBAejVsQ2MRg66iBBcNTgeuoyJ0GDT4RLReo71BsvvhhhJ7dblUFgmtZh7ZWwP/HFw8/H/a2oPm9mt7VppABVsJt3wbT3JcfQGCpwU6l+5xW4nGC7aXojRSTrmarCj7opF/y5t8XIm29i5eVXqypoGzPtvAeb9t4C5dICr4oqA9F3HMREaLBh/l+QXHiFBg5ySIx1xJx9BjJlZgPBlWgCgkbFkU+655sBpZKIOY5J0+LrA/UWx7S2MbH2xtk4g5stgjEzUYfORi04nh0SFq5Gb8QCCBRaJdIQ1Yd86GPnyuP2FbK3x7yvrzdCcEXfgTl0rwblKrrjKj/vTfQk25UnMfV2gx9cFeoQFB5pueuxt25P9OonWq8az991wtq2uWA9N8sEY601DuwiNxcLS1Tfr8f+++dluQGr+mfF0Bq63UOCkUMUV7q+MzV+IhUyNDwxQoXWImmlpBeulPIR+eR2tgWZO6iCCpVdMDEKChgd7xsm6roy7Fy13Ah573FmmhX2CO+f2bEZz9oZ+7fXWJHeORN6FKrvBiqsE58WP3i1a0ORJwr7Q95X1uxAfHeYw4HS3zhrLcLG9Mh723PhtKRhHNeDqWKN1AcyPtacTSBylJ6QmxWLOiG5MQR/L9ogxC9dXKHBSKCPn2Zev7HqtRZeuowuN+UXw3vgor0yl1Ya6HkIU2nUs1LWbDZi68RhThWtPCLm4CC0W6G3uXrTcC3hsOTs+joI61gAvKCBAlNmME+/txNRbA8DhnbfYQYMjnl7whL6v7izs7ElxVXNSDP07qgGXe07cJVOizHroPZ1AYn3OtzZjeDdsOnzRJh/T/PHm76mQVAQ5Dsk5QsnhChWh8WypEfOTz5/uS4D1ZzZABfzpvgSbujfeqD+yMue0y1lU5rN5pLRg24/Yf7bcIlFUTKxJp/yJyFEirq9Fhaux+y/D3Lpou7sunT1CZk3tLCjF2y7ymwBAGxqEzUcuihKoxkeLs+iuqwRnT4mRsC/0fRX6XeBfw3zdPHcvvlIM/ZvXgDPfNncmBThj3kPvaQD43JAEp5+hwAAVFoxPtLvOpb33VOiEDaWgwEmhAjyIzs1ziHYWlOKDPUU2AZGRAz7YU2QRwHgrOZkD2yyq1KRYrJ7U2yboE5PeUIenPjzksESAp9wJGswvmo/0ioMQ7q5N50plTQPyLlS5/Xh3p4k7ei5XQYWQROQ/DkpweIctlJhDzGIFDfaIdcET8r6KGUALJdXQv71zJssC2EKYL/Tt6Tl63d4il+c3Ie+pWPmLckNDdQrVJsz95VL4BVpZunV9VWmcdamIsT3j8C5UmPaZd5ZFKJVgXN6doRf+ovm1gCDuwZ7NJSzsVS0Xg6cnPzGHXlwlmQq5Mzd4UEyQJ8cifs54esGzTijf/ZdhDnO2zHlrGNKaVOvSOTpnOtrPWG0Ixt8Tiy+PX2EuOGk+O1qMczTL0Bnrd1Ws/EW5ocBJoa5ccz85vENk83CB0GngyZ0jvbpcAOtd/tiesVgbYHsSCgsONCVqionvERNzXN7doKFzFPvQz8jEGEHrggklxsnPW7NqhAR5m49e9Pj1OMiviJ8znlzwnCWUsyxdJGYAzUqq/MEqJ0v6ONvPv6b2wEuf/4DPj11y+Rrms6PFCABZb1pZvqti5S/KDQVOClPfaMQnB4rw2WH3T+Z8jQ6hd5XeynHisRboBGxPQud/qcHb34o75decFIunuhM0pKXEY9H2U0xtHSXVerqUiBJPfkKCvJo6z4PvPw6KV9TMIXcveGLNoPLFtHQpbir40i9C60kFBqiw7Hc9kfPTVaezku3lfIkRAIo1dObOhA0loBwnBVmyoxDd53+NN3b85NGUVr5Gh1TTha0rhKvc/E6wFujk8SehB3vG4cN95917UQHkMC4fGKBCmNr119hZUq060P2TllJPfnzvqbdYL5oqd+7kG4lVAd6XrPPj5o/r4dHzeZL4HBigwoS+HRz+3dEMOEc5SI6quNsj5tCZmPmLckE9TgqxZEchcx0RZ8wvoFJNF35mYDwGdGlr6n6uqqnH9M+ET+VmLdBp7eD5ClF6CVyRw7j84aJK3GCoOZXcOdLmIsdX8/34QLHbry917olUvNl7KvV6cVIRmm8kVgV4XzPvBWoycvhwX5FHQ1/u3mDxE3ccsZ71bM7RMOA3BaWYsSnf6Wdfis+rL4ZfpUSBkwLUNxqxbq/nQRNgeYcitBs1uXMkU/fvJ7nFmPlAN4svxRo7OUjOBKiaX88dYtdJsaeVJkgWF0PWk/Kun8rQZOQQGKDyuJBjuCYQf3/kbugilHvy82Zv4YM9palRw1rR2xNCLnj+OINKjKEvd26wWCbubDtRir+m9hA0DDi2ZxyevXjN6fWEnzwkNqVUBWdBgZMCeLK8irk5I+6wu+Al611l3oUqphPHtRsNOHiuAoO63VpN3vwEvP/sL3j3u3NOn8PINb+ee1806bsSgjwY3hIT60n5Rn0T3t111rRUhSdH6E9DuuLR3q6TfOVMzN5CVxfUD/cVo3fHKIx1c1Fue+wFv7oIDZ7s1wnx0eGiBlKsFzx/nUHl6BypDQ1C9c1Gp491t/eGZdanO713TUYO//vB+UxcVwEZocBJES5Uuj+DjhfTOhgzht9u92+sd5VCatnsP/eLReAE3DoBS31nmtIl2mVg5qlrNxpkMeTQLyGKefbgR/vPI1Qd5FHQFK4JxAv3d/XgGeTBncrz9nplObgO0zkOmPbZMawNECefw2ECtqHOYjkM1qVRxOKvM6iAW+fIg+cqkHu+HEYO2HzE9QSd+ePcy/2T6hwpVUDW0kiaHP7GG29g4MCBCAsLQ5s2bey2UalUNj9r1661aHPy5EkMHToUoaGhaN++PRYuXAiOs/xq7t69G8nJyQgJCUGXLl1sngMAMjMzkZiYCI1Gg8TERGzdutWmzerVq5GQkICQkBAkJydj79697h8AkQiZcu5IXROH7ELHC5OyFNMTMsvtyjXHX87zv/zK9BzR4RrXjewY0LWtoERId3k65NBk5JB7rsKjquTZhXrmkgvVNxs9LuRYU9eEoW9+J2oRUF8QWnn+d33a201uHZsUw/wcYiRGC1lSg5/J5q33ypcFLKXWZOTw7q6zmP7ZMbz73Tms/v4cKp2UGuBFCpzgwpOq906Jw6linCfFJmngVF9fj9///vd44YUXnLbbsGEDSktLTT/PPPOM6W8GgwEjR45EXFwcjhw5glWrVmH58uVYsWKFqU1RURHGjh2LIUOGID8/H/PmzcPMmTORmZlpapObm4sJEyYgLS0NJ06cQFpaGp544gkcOnRr0dotW7Zg9uzZeOWVV5Cfn48hQ4ZgzJgxKCkpEfGoCJeWEu/xDKDqGw0en0SFzHJrH2k/sbvJyOGfuRfYnsTNfQ4MUGHpY3c7bSNGYOVuYAc09xoMXrYLT647aKpKfu8b2djxwxXm5xBS/VpMji7IcjzBOZOaFIv/G9iZqe2gbrfZrUbe5bbWzK8nxtISQgp3+mImmz/OoNpZUIrkv2fjrZzTuCawGKq7AYg7y8+wUNpwqr3zpNirN7hD0qG6119/HQDw8ccfO23Xpk0b6HT2p+t++umnqK2txccffwyNRoOkpCScPn0aK1asQHp6uqmHqlOnTli5ciUAoEePHjh69CiWL1+Oxx9/HACwcuVKjBw5EhkZGQCAjIwM7N69GytXrsSmTZsAACtWrMCzzz6L5557zvSYb775BmvWrMGSJUs8PRxuCw4KwJQhCR7NqhNjQcV2Eexfpjahwfjy+GWbYb/DRZWoclKXxFy5gB4ua6lJsVj7dB8s2PajRQVeXYQGC8bfBQCYutHDauNuBnaOhloqaxow7bN8PH/pGjLGuu4NkWJhUnOOcnfsfZacFT2U88Vy1F2x2HDAdSCviwhxuBCvkBpYnt7JC328L2ay+dMMqp0FpR6dJ9wNQFwlpbtbUFVJw6li1QSTgizqOM2YMQPR0dG49957sXbtWhiNt6ZX5+bmYujQodBobt3djx49GleuXEFxcbGpzahRoyyec/To0Th69CgaGhqctjlw4ACA5t6xvLw8mzajRo0ytbGnrq4OBoPB4kcKGWMT8fx9CR6tbeTxgooCblrf2HHK7h2CkBO/p3c9qUmx2P/yAxa9BPtffgCpSbGCehsccSewYxlqeX9PEf53/IrL3hupu9OdbaP5Z4k/wVkHcd4eKnIHfyFxRhehgZHj7L4XA7oIGxb29DPt7uO9PfQi5Tp63uJJj667PULm+N47e+tLuttjroTh1CYjh/1nyvFy5knZ1gTzeeC0aNEi/Oc//0FOTg4mTpyIuXPnYvHixaa/6/V6xMRY5hHw/9fr9U7bNDY2ory83Gkb/jnKy8vR1NTktI09S5YsgVarNf107NhRyO4LkjE2ET//fQx+18ezGU3unkTdfZz5BZT1xO9JOQJzzk7go+7y7G7FnYsYay/RzC35Lrun5dCdrq++qeiih/yFxNlq77WNRouFnu99IxuLvvrRVPbC1bAw/1xi1MdxNYTjiBw+K0rjbo+umAFIfkmV3eHBax6kXsh5OJUfmntq/SGnw6IedwJ4SHDgtGDBArsJ3eY/R48eZX6+V199FSkpKejVqxfmzp2LhQsX4s0337Roo7IqPc0nhpv/3t021r9jaWMuIyMD1dXVpp+LFz1f18qZ4KAALH+iF9Y+3cflnbIj7p5EWZIh7TG/gCZ3jkRUuOu7Jb4cgZTcrR7tyUWQNfi0jjPs9d70S4iCLsL9PCsxVNbUMxc9lCtHFxLtb3f11kteVNY0YP3+YlNACwBrn+5jUzGfJ+aF1FmPgaPXVmoBTqHEzrFz90ZRrABkxw9XnKZn8GtmurOf1hXS+Zw9XwdN9nqunfFVErvgHKcZM2Zg4sSJTtvEx8e7uz0YMGAADAYDrl69ipiYGOh0Opsen7KyMgC3ep4ctQkKCkLbtm2dtuGfIzo6GoGBgU7b2KPRaCyGEb3FPI9gx8kr+NdBtgR2T06i/Bp37uAvoHkXqvBor/ZYv7/Y5WOk/lK4Uz3a04ugu0GrvbyiwAAVnuzXyWIKurfwuRCsnwk5zdKxxzovJzpcg7n/OQHAeT6eeb5F3vyRWPXtGXy47zx+NatcL3Z1ddY11eQy9OINQnPsWIqHCvmuRoWrMf/Bu0QrCttk5PDqlwUu23mSvyangpRCZoua81VPquDAKTo6GtHR0a4buik/Px8hISGm8gUpKSmYN28e6uvrERzcPKsrKysLcXFxpgAtJSUFX331lcXzZGVloW/fvlCr1aY22dnZmDNnjkWbgQMHAgCCg4ORnJyM7OxsPProo6Y22dnZePjhh6XaXY/wH/zCK9XMj/GkKqxOQHK4I2XXazEiUccUOEn9pXDnYu7pRbBfQhSiwtWorBE2Owewn+gbHx3u1nZ4wvyCrA1lm2mphKEi8wtJ7rkKptIN5gGt0QhsOXrRImiKCldj/rgeot/JWwd6xeU3sOlwicU2K3U5HKGEJhGzBlmuEql5KgCLH71b1ON8uKiS+Rwh95sSFkKHRX2dxC7prLqSkhJUVlaipKQETU1NOH78OADg9ttvR6tWrfDVV19Br9cjJSUFoaGh+O677/DKK6/gT3/6k6kXZ9KkSXj99dcxefJkzJs3D2fOnMHixYvxt7/9zTSENnXqVLz77rtIT0/HlClTkJubi/Xr15tmywHArFmzcN9992HZsmV4+OGH8eWXXyInJwf79u0ztUlPT0daWhr69u2LlJQUfPDBBygpKcHUqVOlPEweE1ImwJOqsO4UDbR25uqviA7XQBcRgqsG387sYL2Yzx/XA9GtNaLMDgoMUOHvDydh2mf5bj+H+YlSyoBEhebhqpCgQIcX5CYjp5hZOkIIuRjxAe20z2xnX1XVNGD6Z/lYE6ASPYCx7jGYMfx2v5jJJoSrhYXtzf5kDbJYlluJDFNjyWPiBk2AdyfRyIGQ/ZVDT6qkgdPf/vY3fPLJJ6b/9+7dGwDw3Xff4f7774darcbq1auRnp4Oo9GILl26YOHChZg+fbrpMVqtFtnZ2Zg+fTr69u2LyMhIpKenIz093dQmISEBO3bswJw5c/Dee+8hLi4O77zzjqkUAQAMHDgQmzdvxquvvor58+eja9eu2LJlC/r3729qM2HCBFRUVGDhwoUoLS1FUlISduzYgc6dPZt9JTUhi+F62rXLn0jczR5497uzePe7s2gTpjad2FytkScV1qm5kwcliLotY3vG4flL19wuL2F+omS9K3bX0sfudjq1XOh6h0oh1sVIjDIgrOQ09OItQhYW7pcQJSjIAhwPi7YJVeP/BsVjxvDmNTnFXjeQ9fPXNjxYcTcl9gj5vsmhJ1XFWZfgJh4xGAzQarWorq5GRESEV16zychh8LJdzD1Bb0/shYd7uT8zz15Xd1S4Go/36YAPf1s80tWHir/ItglTWyTferv2D38HCti/6Es5y2THD6V49csCi6T7AJVtYrj5Num0Idj30nCLk7KntWbsEfo+2PtMWF9clIT/TokZkG6aMqDFBTZSW/TVj0zD/m9P7IV2rUPw5LqDLtvae5+cBUY7C0qxYFuhZa9sRAgWjHf/PMZ6Tl89qY+oayD6Csv3rU2oGu891QcDukhX3oL1+k1r1fkB/q6f9eLpScVrwHmBu+TOkS6TVoFbd3ghQQH49Ln+KP+1zifDC/wdpXWhzJjfCmVKGcCN7RmL0UmWx7Gqph7TP3McyNnrvRmZqLMJQD3xuz7tsfixnggOsp106+gCwn8m3t11Fhv2F+HazQZcu9mAt3LOYPORiz6/QxTKvCdNLP6QiyInOwtKmYImoLlHw5PlRhz15jm6adEbajF14zGsdfPGi6V3//n7EvwiaALYeq6XPn43Bt0uXX61EBQ4+YnUpFjMHN4V7+xyvbitUYRORkcnEvOgav/ZX5wutsuheWHSAJXKox4wcTiq4iMte8dxTYDt0ICz7unDRZWiBU0A8Pmxy9h/rsLm9Vwl1WYX6rEy5zRzkq7YwxtiY5m95ij3xR5/yEWRC6HFKatq6kVfbqTJyOHlL046bZPxxUm3h2gdff6iwtX4+8NJGNszTvBzypmj/ZXD0Jw1Cpz8AH8B+knPtnjuoaIKDLnjNlFe01HeS0rXtopYUNLxSvO+K+svdMkKKY6fdbDjKqn2vUm9sWj7Kb9bosX8vcgu1OO/x69YDK3qtCGYP64HFm0/5XcJ8nImdBbWou2F2P2XYaJOZDh4vsLlDUvVjQYcPF/hdk+JPy1fw0Ip+0uBk8LZuwC55tmHkPWix3rnVn69zu66dlJzVTuELzAndVKvPUISfaXoyTAPdoZ3j3FZHfyV/xY4XYPQPEm3+ma9bNegsod/L1K6tsUr4xLtntQDAlR+lyAvZ0JvFvgacmJOZOArx7O082SIqaUl/Sthf32+5ApxnzuVVgF49KEUsi4Zy/IQASpg0Xb769pJjeWuVe5VrwGgqqZOkoFFPtj5V26xy+PEunCzPyzRYm8JHzkvY+GP3LlZKLteK/L7xPoZlednmbiPepwUyt1KqyoVcG+8e0MGQmumsNRBsb4+lnqx10FffVPUdr6ws6AU0z/Ll/TUfKHyhmjPJWSJFrnfdVpTyjCDP3CnDAcfbIn1PqV0iXaaw2nejvgX6nFSKHcXoOQ8WANOSM0UnqM7PGfnKE/WYBKCde09d9fok5q7wbNQnaPCPH4Ofs00f1mixRFni0oT8Ziv2eeKvfX6xHifBnRtizZhztfdbBOmxgCF3QAQ1yhwUihPLizuPtbdZG/rBSXnj+vhco04bwyRsV7EK2/Ui7ZwqJjcDZ5Z8RectJR4txeU5p8HaM4fYV22h2agEVf4mzJnn00pc8wCA1SY0LeD0zYT+nag4NkP0VCdQnlyYXH3saz1n+y1M0/423rsEtPzeDpE5mq6O+tF/D2z7ng5zfySslfG/IITHBQgqE5Ym1A1rt28lfPUEpZoIb7BMutRqu9rk5HDthPO8zE9WeKKyBcFTgrlzrpxHl+UWL/7Ltp5Y4iMZeafO8dQTjO/pOyVsb7gpCbFYs6Ibngr54zLx743qQ8CAlQtaokW4jsssx6lIGRyidLy9YhzFDgpVGCACuPviWVe70yMi1L5r3WuGzG0Yx0iY21njXUhz8AAFZLaRwgKnLy59pgrrAmyuggNahuNqL7R4LCXJyZCg3880ctpBfcZw7th0+GLFktLWD+PThuCAS5yRuRU6E7uRTiJMN6cyq6EOnVEGhQ4KRRLN7E5MS5KYlXeZR0iY21nTsjMvyYjh29PlQl+DbnM/GKZtThnRDfMGN4N2YV6p708C8bf5bLWTGCACgvGJzpd2481MJfDDDSlFOEk8iR2JXKiHJQcrlCsicEzht2OTVMGYN9Lwz2+GPRLiHI5iyQyTO1yKJDvKXHGehYMKyEz//6VW+wySd0ZOdxJOpq1GKsNwdqn+2DWiDtMa8mJUb9GzDo4vpyBJqQeGSH2uKpTZ282H/EP1OOkUKwX7W4xrbzaK8ISh1gvoCpmnouQ7nNP6xPJ5U7SXu9NcudI5F2osqjI7qyXR8iQlRx6izwhtB4ZIfawLARN+Xr+iQInhfJFNzHLYrLXbjQwDWFJleci5Li4W59IjjO/zHM7dhaUYuib3zkcgrJ+b9wZslLCsgiOCOmVVOo+Eu9ITYrFn+5LwLq9RRa91wEqYMqQBBry9VMUOClUcudIBKhsK2+bUwEovXYTuecqROkREDsZUoqeC1cJ0+ZBT3LnSLyx45Sg4Tq5z/xiTYx31b60uhZTNx4z5UjJcV/dRUm9RCw7C0rxwZ4im+8PxwEf7ClC706RFDz5IcpxUqi8C1UuL/gcgPT/nBBtDTgpernEznMxryhs/UzWQU9wUACmDElw+nzhwYEW/5fz2mOuhqAAy4rsLJXH38o5g0FLvbd+oDdQUi8Rg9DvG/EfFDgplNC7YTGSXpWSDOkogTkyXI33JvW2CHoyxibi+fsSHC4B0zokCHNGdMPbE3uJlmQvFaFL4rBOMNAb/CthWimfYyJv7ixBRfwDBU4KJfRuWIw7ICG9Ob6WmhSL+eMSERUebPpdZU0DFm0/ZRMAZIxNxNsTe9t9nquGOqzMOQNNUIDs1x4TOgQlNPj2l7tnJX2OiXwpfci3ycgh91yFLJeTkjsKnBTK1V2zPWLcAYk5HV1KOwtKMf2zYzbVx+31vDUZOSzeccru8yipy13oEJSQ4Nvf7p6d9Ur+cVA8tKHBsn+/iW8pech3Z0EpBi/bhSfXHcSszcdFS+doKSg5XKFYih864ukdkJhJ3VJUbhY63dxfZlkJSYxnaW+PXO+e3eFonbP1+4uxfn8xFcMkTgn9vsmF0AkkxBb1OCmYo7tmV8S4AxIjqVuqux6huQdK73LnmQ9BWbM3BOWsvSNyvHv2RGCACtU367FhfzFT7yQhPP774+img4P8hnwpoV0cFDgpXGpSLPa9NBzzx/Vgat82PFgWd0BSVm4WGggpucvdHq2d6u5twtR27yRNwXeE83UB/TVhmi4kpCURI6GdcqNoqM4vBAaoEN2abUHch3vFiXIH5MkQm9SVm4UGQkrtcrfmqAseAKqcFC7lh6ze3XUWb+Wctvm7PydM+8swLfE+/jzmiBwr0Hvau07rOzajHic/wRosjEzUefxang6xST2N153p5hPv7eQwaALkHzS4qsnEn8Qd3R0GBqgwa0Q3rH26j806gnJL/BeTvwzTSol6GOxTYjkCT3rXaX3HW6jHyU+wJPq2YViA1xUxEgulvlg5S5y3DoTs3UGZ83QJGG8Rq+dE6evQCeVvw7Riox4Gx5QYdLvbu07rO1qiHic/4SpREWheRy67UO/2a4iVD+KNixVL2QRHd1C8OSO6ybrgpTkxT+JiV3OXMyqG6Rj1MDinxKDb3RpmSuxdkxIFTn5kZKIOYVZLhFh7+YuTbne1i/Xl8dbFik+c3zRlgE3lb5ahrc1HLnr0+t6kxJO4HFAxTPsoad41fr1QZwJUze3kxJ1afErsXZMSDdUpnHmSdpmhFjfqm5y2v3ajAQfPVWBQt2jBryXWl8d8KM2a2BcrvvfEmr8lBftLgrsv8BcS6yEppQzTSsHfvh9SYFkv1Mg1t5PbMRI6JE83ZpYocFIwV/k5jmw8VOxW4CT2l0cbpsY1q9lebcLUWPLY3ZJfrPztDiowQIXx98Ti/T1Fdv/OAZh4b0f874crfp+35I6Wltvlir99P6Sg9GPk6KbSHroxs0SBk0I5m3ruyt4zzTNjhF4UxEpAd3favJj87Q5qZ0EpPnAQNAFAeHAg3so5Y/o/JfjaEnIh8Xf+9v2QQks6RkIm3LQEkuU4FRcX49lnn0VCQgJCQ0PRtWtXvPbaa6ivt6zOW1JSgoceegjh4eGIjo7GzJkzbdqcPHkSQ4cORWhoKNq3b4+FCxeC4ywvu7t370ZycjJCQkLQpUsXrF271mabMjMzkZiYCI1Gg8TERGzdutWmzerVq5GQkICQkBAkJydj7969IhwNcbnKz3Hl17pGt5L4xEhA93TavFj8KSmY5fNQYzWESwm+xBl/+n5IpaUdI6WsU+oNkgVOP/30E4xGI95//338+OOPeOutt7B27VrMmzfP1KapqQnjxo1DTU0N9u3bh82bNyMzMxNz5841tTEYDBg5ciTi4uJw5MgRrFq1CsuXL8eKFStMbYqKijB27FgMGTIE+fn5mDdvHmbOnInMzExTm9zcXEyYMAFpaWk4ceIE0tLS8MQTT+DQoUOmNlu2bMHs2bPxyiuvID8/H0OGDMGYMWNQUlIi1WFyi6v8Axbudh97moAul9kZ/pQU7M7ngRJ8bVG9olv86fshlZZ4jJxNuGlJVJx1142E3nzzTaxZswbnz58HAHz99dd48MEHcfHiRcTFxQEANm/ejMmTJ6OsrAwRERFYs2YNMjIycPXqVWg0zdWxly5dilWrVuHSpUtQqVR46aWXsG3bNpw6dWuF+6lTp+LEiRPIzc0FAEyYMAEGgwFff/21qU1qaioiIyOxadMmAED//v3Rp08frFmzxtSmR48eeOSRR7BkyRKmfTQYDNBqtaiurkZERIQHR8uxL49fxqzNxz16jk1TBrg1LLH/TDmeWn/IZbtPn+1vN4+KddvfntgLD/dqL3j7hPKHOjWefh7c/Sz4E3/4HEiBjotrdIz8B+v126s5TtXV1YiKutVtmZubi6SkJFPQBACjR49GXV0d8vLyMGzYMOTm5mLo0KGmoIlvk5GRgeLiYiQkJCA3NxejRo2yeK3Ro0dj/fr1aGhogFqtRm5uLubMmWPTZuXKlQCA+vp65OXl4eWXX7ZoM2rUKBw4cMDhPtXV1aGurs70f4PBwH5A3OTJmLmnSXy558uZ29kLnOSWF+APScGeHiu5Jq96C60W75g/fD+kRseo5fFa4HTu3DmsWrUK//jHP0y/0+v1iImJsWgXGRmJ4OBg6PV6U5v4+HiLNvxj9Ho9EhIS7D5PTEwMGhsbUV5ejtjYWIdt+NcpLy9HU1OT0zb2LFmyBK+//jrDERAPS5K2PeJ0H7M+zn47Oc7OUHpSsLufB15x+Q3Rt0kpqCKya0r/fngDHaOWRXCO04IFC6BSqZz+HD161OIxV65cQWpqKn7/+9/jueees/ibSmV7MuI4zuL31m340UUx2lj/jqWNuYyMDFRXV5t+Ll6Uvmiis7F1Z8RI4mM9OThq5yrBnIP/5QVIzd3PA29lzukWmyQul5w7QohyCO5xmjFjBiZOnOi0jXkP0ZUrVzBs2DCkpKTggw8+sGin0+kskrMBoKqqCg0NDaaeH51OZ9PjU1ZWBgAu2wQFBaFt27ZO2/DPER0djcDAQKdt7NFoNBbDiN7iqGifI1Hhauz+yzAEB3k2H2BAl7ZoY6f+krnIMDUGdKG7L29y9Hlw9V7xWmqvitJr8RBCvE/wVTQ6Ohrdu3d3+hMS0pxzcfnyZdx///3o06cPNmzYgIAAy5dLSUlBQUEBSktv3e1mZWVBo9EgOTnZ1GbPnj0WJQqysrIQFxdnCtBSUlKQnZ1t8dxZWVno27cv1Gq10zYDBw4EAAQHByM5OdmmTXZ2tqmN3PAzHOaP6+GybWVNA/IuVHn8moEBKix97G6nbZY8drfDCzA/NOKIt8oR+CN7M17yXh2JOSO6OX0c36ty8FyFdzZURuSWc0cIkT/JyhFcuXIF999/Pzp27Ijly5fjl19+gV6vt+jRGTVqFBITE5GWlob8/Hx8++23ePHFFzFlyhRTRvukSZOg0WgwefJkFBQUYOvWrVi8eDHS09NNQ2hTp07FhQsXkJ6ejlOnTuGjjz7C+vXr8eKLL5pea9asWcjKysKyZcvw008/YdmyZcjJycHs2bNNbdLT0/Hhhx/io48+wqlTpzBnzhyUlJRg6tSpUh0mjwUGqBDdmq3HS6y75tSkWKx9ug90EZYXk1htCNa6GAqkoRFp2VugNz46nOmx0z9reXWdWlotHkKI5yRLDs/KysLZs2dx9uxZdOjQweJvfP5RYGAgtm/fjmnTpmHQoEEIDQ3FpEmTsHz5clNbrVaL7OxsTJ8+HX379kVkZCTS09ORnp5uapOQkIAdO3Zgzpw5eO+99xAXF4d33nkHjz/+uKnNwIEDsXnzZrz66quYP38+unbtii1btqB///6mNhMmTEBFRQUWLlyI0tJSJCUlYceOHejcubNUh0kUvrhrdncmCQ2NeB/r+37tZgOmbjyGOSO6Ycbwbi1i2I4qIhNChPJqHaeWwBt1nKw1GTkMXrbL5Uy1fS8N9/kFIPdcBZ5cd9BlO6otJB5Xnw97dBEh+NuDPRAZrmkRU6ypFg8hhPX6TYGTyHwROAG3atEA9u+a5VKLRklBnj/ZWVCKqb99Ptzl74FEk5GjWjyEtGCs12/JcpyIdyllHSEqR6Bc/r6+nb38MEIIsebVyuFEWlTBltjjaiYjKyoISQghFDj5HblXsGUtR0AXZvGIsSg0z3zWo5w/Z4QQIhUKnIhXCSlHQBdm4ZqMHA6eq/htTcHmILrsep3LxwlFsx4JId4gx9xDCpyIV1E5AunsLCjFy1+ctKgU/u53ZxGuCRT9taggJCFEanKd7UrJ4cSrqFKzNPhZc/aWV6mpaxLtdaggJCHEG/iZ4tYjFHKYpEKBE/EqV5WaAbowC9Vk5LBg249MbT3p4KaCkIQQb+BzYe3NvuZ/58uluShwIl7lqhwBAIy/J5YuzAIcLqqE3sCWxxQZHmzx/1htCJ6/LwG6CMtle9qEBqFNmNrid3IrbUEI8U9yX5qLcpyI7Ly/pwi9O0XSBZqRkHyw+eN6QKcNtUm0/GtqD5sETACyS8okhPg/uefCUuBEvIp1WIlKErATkg+m04bana3oqIwFzWwkhHib3HNhaaiOeBXrsJIvu2GVpl9ClM1Qmz2UO0YIUQJXubC+nqRCgRPxKiFdq1SSgE1ggAoLxt/lsh0ldRNClIDPhQVsJ7TIYZIKBU5+psnIIfdcBbYeu4T1e89ja/5l5J6r8NnsA2tCulapJAG71KRYrH26j01CNwBEhqmxlpK6CSEKIuf1VynHyY/YKxbGk0PRMODWsJKr4ToaVhKOX6vQunL4gC60YC0hRHnkuv6qiuM4eXRF+AmDwQCtVovq6mpERER47XX5YmHO3kwV4PNIHbhVrNEZ6iEhhBDiTazXbxqq8wPOioVZ82XRMB4NK/kOP5T75XF5DeESQohS0FCdH3BVLIwnpwV0aVjJ++S67hMhhCgJBU5+QOjsM7nMVgsMUGFQt2gM6hbt603xe46Gcvl1n+QwhEsIIUpAQ3V+QOjsM5qt1rLIfd0nQghREgqc/ABfLMwVXxcNI74h93WfCCFESShw8gN8sTCWzCAqgtjyyH3dJ0IIURIKnPwEXyzMUc9TrAyKhhHfYB2aLS6/IfGWEEKI8lFyuB8xLxamr76Jypp6RLXSQBchj6JhxDf4oVx9da3TkhUrc07jTl0rCq4JIcQJCpz8jKNV7uWoycjJriKsP+KHcl0VHQWak8RHJurofSCEEAcocCI+QTWFvCs1KRZzRnTDWzlnHLaRU50vQgiRK8pxIl7H1xSynunF1xTaWVDqoy3zb/HR4UztKEmcEEIco8CJeBXVFPId1iRxqvNFCCGOUeBEvIpqCvkOnyTuKHuJ6nwRQohrFDgRr6KaQr7DJ4kDsAme+P9TnS9CCHFOssCpuLgYzz77LBISEhAaGoquXbvitddeQ319vUU7lUpl87N27VqLNidPnsTQoUMRGhqK9u3bY+HCheA4y6Gc3bt3Izk5GSEhIejSpYvNcwBAZmYmEhMTodFokJiYiK1bt9q0Wb16NRISEhASEoLk5GTs3btXhKNBeDRc5Ft8vS+dVb0vHdX5IoQQJpLNqvvpp59gNBrx/vvv4/bbb0dBQQGmTJmCmpoaLF++3KLthg0bkJqaavq/Vqs1/dtgMGDkyJEYNmwYjhw5gtOnT2Py5MkIDw/H3LlzAQBFRUUYO3YspkyZgo0bN2L//v2YNm0abrvtNjz++OMAgNzcXEyYMAGLFi3Co48+iq1bt+KJJ57Avn370L9/fwDAli1bMHv2bKxevRqDBg3C+++/jzFjxqCwsBCdOnWS6lC1KK5qCqnQfBGn4SLPOCv1YF7vi/97cudI5F2owpfHL1NpCEIIcULFWXfdSOjNN9/EmjVrcP78+VsboFJh69ateOSRR+w+Zs2aNcjIyMDVq1eh0WgAAEuXLsWqVatw6dIlqFQqvPTSS9i2bRtOnTpletzUqVNx4sQJ5ObmAgAmTJgAg8GAr7/+2tQmNTUVkZGR2LRpEwCgf//+6NOnD9asWWNq06NHDzzyyCNYsmQJ0z4aDAZotVpUV1cjIiKC7cC0MPysOgAWwRN/maaeD88ILfVApSEIIYT9+u3VHKfq6mpERdn2JMyYMQPR0dG49957sXbtWhiNRtPfcnNzMXToUFPQBACjR4/GlStXUFxcbGozatQoi+ccPXo0jh49ioaGBqdtDhw4AACor69HXl6eTZtRo0aZ2thTV1cHg8Fg8UOc44eLYiJouEhsQks9UGkIQggRxmuB07lz57Bq1SpMnTrV4veLFi3Cf/7zH+Tk5GDixImYO3cuFi9ebPq7Xq9HTEyMxWP4/+v1eqdtGhsbUV5e7rQN/xzl5eVoampy2saeJUuWQKvVmn46duzo8lgQnmVnpxc7P/2S0FIPVBqCEEKEExw4LViwwG5Ct/nP0aNHLR5z5coVpKam4ve//z2ee+45i7+9+uqrSElJQa9evTB37lwsXLgQb775pkUblcoy14K/wJr/3t021r9jaWMuIyMD1dXVpp+LFy86bEua8b0cekOdxe+vGuqol8MDQks9UGkIQggRTnBy+IwZMzBx4kSnbeLj403/vnLlCoYNG4aUlBR88MEHLp9/wIABMBgMuHr1KmJiYqDT6Wx6fMrKygDc6nly1CYoKAht27Z12oZ/jujoaAQGBjptY49Go7EYRiTOuerlUIHWS3OX0FIPVBqCEEKEE9zjFB0dje7duzv9CQlpzl25fPky7r//fvTp0wcbNmxAQIDrl8vPz0dISAjatGkDAEhJScGePXssyhhkZWUhLi7OFKClpKQgOzvb4nmysrLQt29fqNVqp20GDhwIAAgODkZycrJNm+zsbFMb4jnq5ZCO0FIPVBqCEEKEkyzH6cqVK7j//vvRsWNHLF++HL/88gv0er1Fj85XX32FdevWoaCgAOfOncOHH36IV155BX/6059MvTiTJk2CRqPB5MmTUVBQgK1bt2Lx4sVIT083DaFNnToVFy5cQHp6Ok6dOoWPPvoI69evx4svvmh6rVmzZiErKwvLli3DTz/9hGXLliEnJwezZ882tUlPT8eHH36Ijz76CKdOncKcOXNQUlJik5dF3Ee9HNIRWhmcKokTQohwktVxysrKwtmzZ3H27Fl06NDB4m98/pFarcbq1auRnp4Oo9GILl26YOHChZg+fbqprVarRXZ2NqZPn46+ffsiMjIS6enpSE9PN7VJSEjAjh07MGfOHLz33nuIi4vDO++8Y6rhBAADBw7E5s2b8eqrr2L+/Pno2rUrtmzZYqrhBDSXLKioqMDChQtRWlqKpKQk7NixA507d5bqMLU41MshHb4y+Asbj0EF+6UezCuDC21PCCHEy3WcWgKq4+Rck5HD4GW7XBbA3PfScLpgu4nqOBFCiHCs128KnERGgZNrVABTes4qh4vRnhBC/A0FTj5CgRMb6uUghBAiJ6zXb8lynAhxxt56adTLQQghRO4ocCI+ExigQkrXtr7eDEIIIYSZV9eqI4QQQghRMgqcCCGEEEIYUeBECCGEEMKIAidCCCGEEEYUOBFCCCGEMKLAiRBCCCGEEQVOhBBCCCGMKHAihBBCCGFEgRMhhBBCCCMKnAghhBBCGFHgRAghhBDCiAInQgghhBBGFDgRQgghhDCiwIkQQgghhBEFToQQQgghjChwIoQQQghhRIETIYQQQggjCpwIIYQQQhhR4EQIIYQQwogCJ0IIIYQQRhQ4EUIIIYQwosCJEEIIIYQRBU6EEEIIIYwocCKEEEIIYUSBEyGEEEIIIwqcCCGEEEIYSRo4jR8/Hp06dUJISAhiY2ORlpaGK1euWLQpKSnBQw89hPDwcERHR2PmzJmor6+3aHPy5EkMHToUoaGhaN++PRYuXAiO4yza7N69G8nJyQgJCUGXLl2wdu1am+3JzMxEYmIiNBoNEhMTsXXrVps2q1evRkJCAkJCQpCcnIy9e/eKcCQIIYQQ4g8kDZyGDRuGf//73/j555+RmZmJc+fO4Xe/+53p701NTRg3bhxqamqwb98+bN68GZmZmZg7d66pjcFgwMiRIxEXF4cjR45g1apVWL58OVasWGFqU1RUhLFjx2LIkCHIz8/HvHnzMHPmTGRmZpra5ObmYsKECUhLS8OJEyeQlpaGJ554AocOHTK12bJlC2bPno1XXnkF+fn5GDJkCMaMGYOSkhIpDxMhhBBClILzoi+//JJTqVRcfX09x3Ect2PHDi4gIIC7fPmyqc2mTZs4jUbDVVdXcxzHcatXr+a0Wi1XW1trarNkyRIuLi6OMxqNHMdx3F//+leue/fuFq/1/PPPcwMGDDD9/4knnuBSU1Mt2owePZqbOHGi6f/9+vXjpk6datGme/fu3Msvv8y8j9XV1RwA0/YTQgghRP5Yr99ey3GqrKzEp59+ioEDB0KtVgNo7gVKSkpCXFycqd3o0aNRV1eHvLw8U5uhQ4dCo9FYtLly5QqKi4tNbUaNGmXxeqNHj8bRo0fR0NDgtM2BAwcAAPX19cjLy7NpM2rUKFMbe+rq6mAwGCx+CCGEEOKfJA+cXnrpJYSHh6Nt27YoKSnBl19+afqbXq9HTEyMRfvIyEgEBwdDr9c7bMP/31WbxsZGlJeXO23DP0d5eTmampqctrFnyZIl0Gq1pp+OHTs6PyCEEEIIUSzBgdOCBQugUqmc/hw9etTU/i9/+Qvy8/ORlZWFwMBA/OEPf7BI7FapVDavwXGcxe+t2/CPF6ON9e9Y2pjLyMhAdXW16efixYsO2xJCCCFE2YKEPmDGjBmYOHGi0zbx8fGmf0dHRyM6Ohp33HEHevTogY4dO+LgwYNISUmBTqezSM4GgKqqKjQ0NJh6fnQ6nU2PT1lZGQC4bBMUFIS2bds6bcM/R3R0NAIDA522sUej0VgMIxJCCCHEfwnucYqOjkb37t2d/oSEhNh9LN8LVFdXBwBISUlBQUEBSktLTW2ysrKg0WiQnJxsarNnzx6LEgVZWVmIi4szBWgpKSnIzs62eK2srCz07dvXlE/lqM3AgQMBAMHBwUhOTrZpk52dbWpDCCGEkBZOquz0Q4cOcatWreLy8/O54uJibteuXdzgwYO5rl27mmbINTY2cklJSdwDDzzAHTt2jMvJyeE6dOjAzZgxw/Q8165d42JiYrgnn3ySO3nyJPfFF19wERER3PLly01tzp8/z4WFhXFz5szhCgsLufXr13NqtZr7/PPPTW3279/PBQYGckuXLuVOnTrFLV26lAsKCuIOHjxoarN582ZOrVZz69ev5woLC7nZs2dz4eHhXHFxMfN+06w6QgghRHlYr9+SBU4//PADN2zYMC4qKorTaDRcfHw8N3XqVO7SpUsW7S5cuMCNGzeOCw0N5aKiorgZM2ZYlB7gn2vIkCGcRqPhdDodt2DBAlMpAt7333/P9e7dmwsODubi4+O5NWvW2GzTf/7zH+7OO+/k1Go11717dy4zM9OmzXvvvcd17tyZCw4O5vr06cPt3r1b0H5T4EQIIYQoD+v1W8VxViW4iUcMBgO0Wi2qq6sRERHh680hhBBCCAPW6zetVUcIIYQQwogCJ0IIIYQQRhQ4EUIIIYQwosCJEEIIIYQRBU6EEEIIIYwocCKEEEIIYUSBEyGEEEIIIwqcCCGEEEIYUeBECCGEEMKIAidCCCGEEEYUOBFCCCGEMKLAiRBCCCGEEQVOhBBCCCGMKHAihBBCCGEU5OsNIJ5pMnI4XFSJsuu1aNc6BP0SohAYoPL1ZhFCCCF+iQInBdtZUIrXvypEaXWt6Xex2hC89lAiUpNifbhlhBBCiH+ioTqF2llQihc2HrMImgBAX12LFzYew86CUh9tGSGEEOK/KHBSoCYjh9e/KgRn52/8717/qhBNRnstCCGEEOIuCpwU6HBRpU1PkzkOQGl1LQ4XVXpvowghhJAWgAInBSq77jhocqcdIYQQQthQ4KRA7VqHiNqOEEIIIWwocFKgfglRiNWGwFHRARWaZ9f1S4jy5mYRQgghfo8CJwUKDFDhtYcSAcAmeOL//9pDiVTPiRBCCBEZBU4KlZoUizVP94FOazkcp9OGYM3TfaiOEyGEECIBKoCpYKlJsRiZqKPK4YQQQoiXUOCkcIEBKqR0bevrzSCEEEJaBBqqI4QQQghhRIETIYQQQggjCpwIIYQQQhhR4EQIIYQQwogCJ0IIIYQQRpIGTuPHj0enTp0QEhKC2NhYpKWl4cqVKxZtVCqVzc/atWst2pw8eRJDhw5FaGgo2rdvj4ULF4LjOIs2u3fvRnJyMkJCQtClSxeb5wCAzMxMJCYmQqPRIDExEVu3brVps3r1aiQkJCAkJATJycnYu3evCEeCEEIIIf5A0sBp2LBh+Pe//42ff/4ZmZmZOHfuHH73u9/ZtNuwYQNKS0tNP88884zpbwaDASNHjkRcXByOHDmCVatWYfny5VixYoWpTVFREcaOHYshQ4YgPz8f8+bNw8yZM5GZmWlqk5ubiwkTJiAtLQ0nTpxAWloannjiCRw6dMjUZsuWLZg9ezZeeeUV5OfnY8iQIRgzZgxKSkokOkKEEEIIURIVZ911I6Ft27bhkUceQV1dHdRqdfMGqFTYunUrHnnkEbuPWbNmDTIyMnD16lVoNBoAwNKlS7Fq1SpcunQJKpUKL730ErZt24ZTp06ZHjd16lScOHECubm5AIAJEybAYDDg66+/NrVJTU1FZGQkNm3aBADo378/+vTpgzVr1pja9OjRA4888giWLFnCtI8GgwFarRbV1dWIiIhgPziEEEII8RnW67fXcpwqKyvx6aefYuDAgaagiTdjxgxER0fj3nvvxdq1a2E0Gk1/y83NxdChQ01BEwCMHj0aV65cQXFxsanNqFGjLJ5z9OjROHr0KBoaGpy2OXDgAACgvr4eeXl5Nm1GjRplamNPXV0dDAaDxQ8hhBBC/JPklcNfeuklvPvuu7hx4wYGDBiA//3vfxZ/X7RoER544AGEhobi22+/xdy5c1FeXo5XX30VAKDX6xEfH2/xmJiYGNPfEhISoNfrTb8zb9PY2Ijy8nLExsY6bKPX6wEA5eXlaGpqctrGniVLluD111+3+T0FUIQQQohy8NdtVwNxggOnBQsW2A0UzB05cgR9+/YFAPzlL3/Bs88+iwsXLuD111/HH/7wB/zvf/+DStW8nhofIAFAr169AAALFy60+D3flsfvlPnv3W1j/TuWNuYyMjKQnp5u+v/ly5eRmJiIjh07OnwMIYQQQuTp+vXr0Gq1Dv8uOHCaMWMGJk6c6LSNeQ9RdHQ0oqOjcccdd6BHjx7o2LEjDh48iJSUFLuPHTBgAAwGA65evYqYmBjodDqbHp+ysjIAt3qeHLUJCgpC27ZtnbbhnyM6OhqBgYFO29ij0WgshhFbtWqFixcvonXr1k4DLqEMBgM6duyIixcvtqjcqZa63wDte0vc95a630DL3feWut+A/Pad4zhcv34dcXFxTtsJDpz4QMjdjQKa84Icyc/PR0hICNq0aQMASElJwbx581BfX4/g4GAAQFZWFuLi4kwBWkpKCr766iuL58nKykLfvn1N+VQpKSnIzs7GnDlzLNoMHDgQABAcHIzk5GRkZ2fj0UcfNbXJzs7Gww8/zLyPAQEB6NChA3N7oSIiImTxAfO2lrrfAO17S9z3lrrfQMvd95a634C89t1ZT5MJJ5FDhw5xq1at4vLz87ni4mJu165d3ODBg7muXbtytbW1HMdx3LZt27gPPviAO3nyJHf27Flu3bp1XEREBDdz5kzT81y7do2LiYnhnnzySe7kyZPcF198wUVERHDLly83tTl//jwXFhbGzZkzhyssLOTWr1/PqdVq7vPPPze12b9/PxcYGMgtXbqUO3XqFLd06VIuKCiIO3jwoKnN5s2bObVaza1fv54rLCzkZs+ezYWHh3PFxcVSHSZm1dXVHACuurra15viVS11vzmO9r0l7ntL3W+Oa7n73lL3m+OUu++SBU4//PADN2zYMC4qKorTaDRcfHw8N3XqVO7SpUumNl9//TXXq1cvrlWrVlxYWBiXlJTErVy5kmtoaLB5riFDhnAajYbT6XTcggULOKPRaNHm+++/53r37s0FBwdz8fHx3Jo1a2y26T//+Q935513cmq1muvevTuXmZlp0+a9997jOnfuzAUHB3N9+vThdu/eLdIR8YxSP2Ceaqn7zXG07y1x31vqfnNcy933lrrfHKfcfZcscCLiqq2t5V577TVTb11L0VL3m+No31vivrfU/ea4lrvvLXW/OU65++7VApiEEEIIIUpGi/wSQgghhDCiwIkQQgghhBEFToQQQgghjChwIoQQQghhRIGTAqxevRoJCQkICQlBcnIy9u7d6+tNEmTPnj146KGHEBcXB5VKhf/+978Wf+c4DgsWLEBcXBxCQ0Nx//3348cff7RoU1dXhz//+c+Ijo5GeHg4xo8fj0uXLlm0qaqqQlpaGrRaLbRaLdLS0nDt2jWJ986xJUuW4N5770Xr1q3Rrl07PPLII/j5558t2vjrvq9ZswY9e/Y0FbZLSUnB119/bfq7v+63tSVLlkClUmH27Nmm3/nrvi9YsAAqlcriR6fTmf7ur/vNu3z5Mp5++mm0bdsWYWFh6NWrF/Ly8kx/98f9j4+Pt3nPVSoVpk+fDsA/9xmAdAUwiTj4opzr1q3jCgsLuVmzZnHh4eHchQsXfL1pzHbs2MG98sorXGZmJgeA27p1q8Xfly5dyrVu3ZrLzMzkTp48yU2YMIGLjY3lDAaDqc3UqVO59u3bc9nZ2dyxY8e4YcOGcffccw/X2NhoapOamsolJSVxBw4c4A4cOMAlJSVxDz74oLd208bo0aO5DRs2cAUFBdzx48e5cePGcZ06deJ+/fVXUxt/3fdt27Zx27dv537++Wfu559/5ubNm8ep1WquoKCA4zj/3W9zhw8f5uLj47mePXtys2bNMv3eX/f9tdde4+666y6utLTU9FNWVmb6u7/uN8dxXGVlJde5c2du8uTJ3KFDh7iioiIuJyeHO3v2rKmNP+5/WVmZxfudnZ3NAeC+++47juP8c585juo4yV6/fv24qVOnWvyue/fu3Msvv+yjLfKMdeBkNBo5nU7HLV261PS72tpaTqvVcmvXruU4rrl6vFqt5jZv3mxqc/nyZS4gIIDbuXMnx3EcV1hYyAGwqASfm5vLAeB++uknifeKTVlZGQfAVFS1Je07x3FcZGQk9+GHH7aI/b5+/TrXrVs3Ljs7mxs6dKgpcPLnfX/ttde4e+65x+7f/Hm/OY7jXnrpJW7w4MEO/+7v+8+bNWsW17VrV85oNPr1PtNQnYzV19cjLy8Po0aNsvj9qFGjcODAAR9tlbiKioqg1+st9lGj0WDo0KGmfczLy0NDQ4NFm7i4OCQlJZna5ObmQqvVon///qY2AwYMgFarlc2xqq6uBgBERUUBaDn73tTUhM2bN6OmpgYpKSktYr+nT5+OcePGYcSIERa/9/d9P3PmDOLi4pCQkICJEyfi/PnzAPx/v7dt24a+ffvi97//Pdq1a4fevXtj3bp1pr/7+/4DzderjRs34o9//CNUKpVf7zMFTjJWXl6OpqYmxMTEWPw+JiYGer3eR1slLn4/nO2jXq9HcHAwIiMjnbZp166dzfO3a9dOFseK4zikp6dj8ODBSEpKAuD/+37y5Em0atUKGo0GU6dOxdatW5GYmOj3+71582YcO3YMS5YssfmbP+97//798c9//hPffPMN1q1bB71ej4EDB6KiosKv9xsAzp8/jzVr1qBbt2745ptvMHXqVMycORP//Oc/Afj3+87773//i2vXrmHy5MkA/Hufg3zyqkQQlUpl8X+O42x+p3Tu7KN1G3vt5XKsZsyYgR9++AH79u2z+Zu/7vudd96J48eP49q1a8jMzMQzzzyD3bt3m/7uj/t98eJFzJo1C1lZWQgJCXHYzh/3fcyYMaZ/33333UhJSUHXrl3xySefYMCAAQD8c78BwGg0om/fvli8eDEAoHfv3vjxxx+xZs0a/OEPfzC189f9B4D169djzJgxiIuLs/i9P+4z9TjJWHR0NAIDA22i6rKyMpsoXqn4WTfO9lGn06G+vh5VVVVO21y9etXm+X/55RefH6s///nP2LZtG7777jt06NDB9Ht/3/fg4GDcfvvt6Nu3L5YsWYJ77rkHb7/9tl/vd15eHsrKypCcnIygoCAEBQVh9+7deOeddxAUFGTaLn/cd2vh4eG4++67cebMGb9+zwEgNjYWiYmJFr/r0aMHSkpKAPj/d/3ChQvIycnBc889Z/qdP+8zBU4yFhwcjOTkZGRnZ1v8Pjs7GwMHDvTRVokrISEBOp3OYh/r6+uxe/du0z4mJydDrVZbtCktLUVBQYGpTUpKCqqrq3H48GFTm0OHDqG6utpnx4rjOMyYMQNffPEFdu3ahYSEBIu/+/O+28NxHOrq6vx6vx944AGcPHkSx48fN/307dsXTz31FI4fP44uXbr47b5bq6urw6lTpxAbG+vX7zkADBo0yKbUyOnTp9G5c2cA/v9d37BhA9q1a4dx48aZfufX++y1NHTiFr4cwfr167nCwkJu9uzZXHh4OFdcXOzrTWN2/fp1Lj8/n8vPz+cAcCtWrODy8/NNJRWWLl3KabVa7osvvuBOnjzJPfnkk3anrHbo0IHLycnhjh07xg0fPtzulNWePXtyubm5XG5uLnf33Xf7dMrqCy+8wGm1Wu7777+3mLJ748YNUxt/3feMjAxuz549XFFREffDDz9w8+bN4wICArisrCyO4/x3v+0xn1XHcf6773PnzuW+//577vz589zBgwe5Bx98kGvdurXpXOWv+81xzaUngoKCuDfeeIM7c+YM9+mnn3JhYWHcxo0bTW38df+bmpq4Tp06cS+99JLN3/x1nylwUoD33nuP69y5MxccHMz16dPHNJ1dKb777jsOgM3PM888w3Fc81Td1157jdPpdJxGo+Huu+8+7uTJkxbPcfPmTW7GjBlcVFQUFxoayj344INcSUmJRZuKigruqaee4lq3bs21bt2ae+qpp7iqqiov7aUte/sMgNuwYYOpjb/u+x//+EfTZ/a2227jHnjgAVPQxHH+u9/2WAdO/rrvfI0etVrNxcXFcY899hj3448/mv7ur/vN++qrr7ikpCROo9Fw3bt35z744AOLv/vr/n/zzTccAO7nn3+2+Zu/7rOK4zjOJ11dhBBCCCEKQzlOhBBCCCGMKHAihBBCCGFEgRMhhBBCCCMKnAghhBBCGFHgRAghhBDCiAInQgghhBBGFDgRQgghhDCiwIkQQgghhBEFToQQQgghjChwIoQQQghhRIETIYQQQggjCpwIIYQQQhj9f8fDDalLeVjOAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, compute the current unit usage, plot its histogram weighted by unit pop\n",
      "unit avg and sd usage are 0.92424 0.14557\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to correcting for discontiguity ...\n",
      "CCB cluster 0 with pop 82874.0 originally had use of 1.0065037212789716\n",
      "CCB cluster 1 with pop 90352.0 originally had use of 1.0003609873504258\n"
     ]
    }
   ],
   "source": [
    "#continuing w/option 1 - now we determine original (nonsmoothed) vtd lists for the original HD polygons\n",
    "print(\"This is the main code to determine each HD's pop based on UNIT CP's that intersect the HDpoly\")\n",
    "print(\"We'll later add nearby or jettison faraway contiguous units until we approach the aDP\")\n",
    "print(\"  the HD pops and lists already appropriately include captured corner county clusters\")\n",
    "#see above block for preliminaries\n",
    "nonUnitCounties = list (set([c for c in range(nCounties)]).difference(set(unitCounties)) )\n",
    "areaFrac = [0.5 for u in range(nUnits)]  #default; we will accept unit counties if we capture half their area\n",
    "alter = input(\"enter 1 to enforce areaFrac threshold capture for clusters; helps to even up their usage\")\n",
    "if alter == 1:\n",
    "    for u in range(nUnits):\n",
    "        if allUnits[u] - int(allUnits[u]) == 0.25:  #a county cluster.  Enforce alternate areaFrac's determined above\n",
    "            CCBno = int(allUnits[u] )\n",
    "            areaFrac[u] = CCBareaFrac[CCBno ]\n",
    "            print(\"enforcing areaFrac capture threshold of\",r5(CCBareaFrac[CCBno]),\"for CCBno, uNo\",CCBno,u)\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] - int(allUnits[u]) == 0.25:  #a county cluster.  Enforce alternate areaFrac's determined above\n",
    "        CCBno = int(allUnits[u] )\n",
    "        areaFrac[u] = CCBareaFrac[CCBno ]\n",
    "        #print(\"enforcing areaFrac capture threshold of\",r5(CCBareaFrac[CCBno]),\"for CCBno, uNo\",CCBno,u)\n",
    "# for c in unitCounties:   #UNCOMMENT TO ENFORCE UNIT COUNTIES TO EQUAL USAGE\n",
    "#    areaFrac[allUnits.index(c+0.5)] = unitCareaFrac[ unitCounties.index(c) ]\n",
    "\n",
    "origHDpop, origHDlist = [0. for t in range(nHDs)], [list() for t in range(nHDs)]\n",
    "for jj,t in enumerate(popHDlist):\n",
    "    if jj%500 == 0:\n",
    "        print(\"working on HDno\",t)\n",
    "    hC = HDcountyNo[t]\n",
    "    if hC in allFusedCounties:  #using a swelling technique as original HDpoly's look skewy\n",
    "        origHDpop[t], origHDlist[t] = clusterSwell(hdCP[t], CCBgeom, CCBpop, allUnits, unitCP, unitPop, unitNbrs, aDP)\n",
    "    else:\n",
    "        if hC in unitCounties:\n",
    "            iCP, iClist = countyPop[hC], [allUnits.index(hC+0.5) ] #automatically include the home county if small\n",
    "        else: #must probe in-county list vs HDpoly intersxn\n",
    "            iCP, iClist =  getPolyPop(HDpoly[t], unitCP, unitPop, countyUnitList[hC])\n",
    "        nonCP, nonClist = getNonHCunits(HDpoly[t], hC, allUnits, unitCP, unitPop, allFusedCounties, areaFrac, countyGeom,\n",
    "                                        neighborCountyList, countyUnitList)  #this will miss county clusters; see below\n",
    "        origHDpop[t]  += iCP + nonCP\n",
    "        origHDlist[t] += iClist + nonClist\n",
    "        for j, geo in enumerate(CCBgeom):  #special for corner clusters -- intersection area must clear threshold estbd in a prev block\n",
    "            unitNo = allUnits.index(j+0.25)\n",
    "            if geo.intersection(HDpoly[t]).area >= areaFrac[unitNo] * CCBarea[j]:\n",
    "                origHDpop[t] += unitPop[unitNo]\n",
    "                origHDlist[t].append(unitNo)\n",
    "print(\"all done assigning units to HDs based on original HD polygons\")\n",
    "HDpopExcess = list()\n",
    "for t in popHDlist:\n",
    "    HDpopExcess.append(origHDpop[t] - aDP)\n",
    "print(\"Here is a scatterplot of active HD pop excess vs relative to target\",aDP)\n",
    "plt.scatter(popHDlist, HDpopExcess)\n",
    "plt.axhline(-0.01*aDP,np.min(popHDlist),np.max(popHDlist),ls=\"--\")\n",
    "plt.axhline( 0.01*aDP,np.min(popHDlist),np.max(popHDlist),ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, compute the current unit usage, plot its histogram weighted by unit pop\")\n",
    "origUnitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in origHDlist[t]:\n",
    "        origUnitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(origUnitUse,bins=50,weights=unitPop)\n",
    "origUnitUseAvg, origUnitUseSD = getWeightedAvgAndSD(origUnitUse,unitPop)\n",
    "print(\"unit avg and sd usage are\",r5(origUnitUseAvg), r5(origUnitUseSD) )\n",
    "plt.show()\n",
    "print(\"prior to correcting for discontiguity ...\")\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"with pop\",unitPop[u],\"originally had use of\",origUnitUse[u])\n",
    "\n",
    "HDunitList = [origHDlist[t].copy() for t in range(nHDs) ]  #for safekeeping\n",
    "HDvPop = origHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0fdec9f3-be08-4952-9745-bb0e47e5a04d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111 437864.00000000006\n"
     ]
    }
   ],
   "source": [
    "for t in popHDlist:\n",
    "    if HDvPop[t] < 450000:\n",
    "        print(t,HDvPop[t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6f443a63-ce36-4eba-b104-c84d3114c2de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "437864.00000000006\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = 111\n",
    "print(HDvPop[t])\n",
    "for u in HDunitList[t]:\n",
    "    plotPoly(unitGeom[u])\n",
    "plotPoly(HDpoly[t].intersection(MAP.buffer(1)))\n",
    "plotPoly(hdCP[t].buffer(0.1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "48e27209-e726-47d6-a194-784b1c7640cc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "showing woefully underpopped HDs -- usually a discontiguous large pop in original shape not picked up when contig counties enforced\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"showing woefully underpopped HDs -- usually a discontiguous large pop in original shape not picked up when contig counties enforced\")\n",
    "for t in popHDlist:\n",
    "    if origHDpop[t] < 500000 :\n",
    "        plotPoly(HDpoly[t])\n",
    "        plotPoly(countyGeom[HDcountyNo[t]],0.4)\n",
    "        plotPoly(hdCP[t].buffer(0.1),2)\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70a8d159-68bb-4436-8a07-0ea73d3f5b94",
   "metadata": {},
   "outputs": [],
   "source": [
    "#END OF OPTION 1 -- SKIP AHEAD TO AFTER OPTION 2\n",
    "###########################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "e74fe279-1089-4774-8b1f-2bdc8d30887d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for some states like FL, we move in partial counties before running option 2\n",
      "adding code here to 'push in' state panhandles ...\n",
      "performing squish no 1 for state FL\n",
      "clipPoly 0 intersects [43] counties and a total of 28 units\n",
      "Here is the original cutLine and an alternate based on cut shapes\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are your orig (faint) and squished shapes for clip no 1 out of 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 when ready 1\n"
     ]
    }
   ],
   "source": [
    "print(\"for some states like FL, we move in partial counties before running option 2\")\n",
    "vtdCP = tractCP.copy()\n",
    "#CODE TO SQUISH GEOMETRIES FROM CLIP LINES INTO THE MAP\n",
    "print(\"adding code here to 'push in' state panhandles ...\")\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = trimDF[\"nCutPoly\"][DFrow]\n",
    "#Adding in here trimming ops\n",
    "toSquishUnitsList = list()\n",
    "toSquishGeomsList = list()  #combined list of geoms we will squish (over all squish operations)\n",
    "toSquishCountiesList = list()\n",
    "squishedShapesList = list()   #unit shapes after squishing, list over all squish operations\n",
    "if nClips > 0:\n",
    "    clipPoints = ast.literal_eval(trimDF[\"clipPoints\"][DFrow])   #sets of four points\n",
    "    farPoints  = ast.literal_eval(trimDF[\"farPoints\"][DFrow])    #pairs of points\n",
    "    newFarPoints  = ast.literal_eval(trimDF[\"newFarPoints\"][DFrow])   #pairs\n",
    "    cPts = [Point(clipPoints[i][0],clipPoints[i][1]) for i in range(len(clipPoints)) ]\n",
    "    fPts = [Point(farPoints[i][0],farPoints[i][1]) for i in range(len(farPoints)) ]\n",
    "    nfPts = [Point(newFarPoints[i][0],newFarPoints[i][1]) for i in range(len(newFarPoints)) ]\n",
    "    \n",
    "    for clipNo in range(nClips):\n",
    "        print(\"performing squish no\",clipNo+1,\"for state\",STATE)\n",
    "        n0,n1,n2,n3 = int(4*clipNo), int(4*clipNo+1), int(4*clipNo+2), int(4*clipNo+3)\n",
    "        clipPoly = Polygon([cPts[n0],cPts[n1],cPts[n2],cPts[n3] ] )\n",
    "        cutLine = LineString([cPts[n0],cPts[n1]] )\n",
    "        farLine = LineString([fPts[int(2*clipNo)],fPts[int(2*clipNo+1)] ] )\n",
    "        newFarLine = LineString([nfPts[int(2*clipNo)],nfPts[int(2*clipNo+1)] ])\n",
    "        \n",
    "        toSquishCounties = list()\n",
    "        toSquishUnits = list()\n",
    "        for c in range(nCounties):\n",
    "            if clipPoly.intersects(countyGeom[c]):\n",
    "                toSquishCounties.append(c)\n",
    "                if clipPoly.contains(countyGeom[c]):\n",
    "                    toSquishUnits = toSquishUnits + countyTractList[c]\n",
    "                else:\n",
    "                    for t in countyTractList[c]:\n",
    "                        if clipPoly.contains(vtdCP[t]):\n",
    "                            toSquishUnits.append(t)\n",
    "        print(\"clipPoly\",clipNo,\"intersects\",toSquishCounties,\"counties and a total of\",len(toSquishUnits),\"units\")\n",
    "        toSquishGeomUnion = vtdGeom[toSquishUnits[0]]\n",
    "        toSquishGeoms = list()\n",
    "        for t in toSquishUnits:\n",
    "            toSquishGeomUnion = toSquishGeomUnion.union(vtdGeom[t])\n",
    "            toSquishGeoms.append(vtdGeom[t])\n",
    "        unclippedGeom = MAP.difference(toSquishGeomUnion)\n",
    "        altCutLine = toSquishGeomUnion.buffer(0.001).intersection(unclippedGeom)\n",
    "        print(\"Here is the original cutLine and an alternate based on cut shapes\")\n",
    "        #plotPoly(MAP,0.1)\n",
    "        plotPoly(countyGeom[43])\n",
    "        plotPoly(countyGeom[42])\n",
    "        plotPoly(cutLine.buffer(0.01))\n",
    "        plotPoly(altCutLine,0.2)\n",
    "        plt.show()\n",
    "        # could use altCutLine for a more accurate squish at the squish-no_squish boundary ...\n",
    "        #newShapes = squish(clippedGeomList, altCutLine, farLine, newFarLine)\n",
    "        squishedShapes = squish(toSquishGeoms, cutLine, farLine, newFarLine) #..but may distort results\n",
    "        toSquishUnitsList.append(toSquishUnits)\n",
    "        toSquishGeomsList.append(toSquishGeoms)\n",
    "        toSquishCountiesList.append(toSquishCounties)\n",
    "        squishedShapesList.append(squishedShapes)\n",
    "        for geo in toSquishGeoms:\n",
    "            plotPoly(geo,0.1)\n",
    "            plotPoly(geo.centroid.buffer(0.004),0.1)\n",
    "        for geo in squishedShapes:\n",
    "            plotPoly(geo)\n",
    "            plotPoly(geo.centroid.buffer(0.008),0.4)\n",
    "        print(\"These are your orig (faint) and squished shapes for clip no\",clipNo+1,\"out of\",nClips)\n",
    "        plt.show()\n",
    "        pause = input(\"enter 1 when ready\")     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "e7e21005-dc00-49b1-9719-62e526a12b2e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POINT (-87.19171719578974 30.52313586297459)\n"
     ]
    }
   ],
   "source": [
    "print(tractCP[1111])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "73dbac1b-4bf3-480a-b028-6edf55a5447d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alternate simple approach for Florida Keys - translate all toward county CP\n",
      "I translated 28 vtds toward the center of county 43\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"alternate simple approach for Florida Keys - translate all toward county CP\")\n",
    "c = 43\n",
    "vtdGeom = tractGeom.copy()  #a reset\n",
    "nTranslated = 0\n",
    "newCP = Point(-81, 25.1)\n",
    "for v in countyTractList[c]:\n",
    "    if clipPoly.contains(tractCP[v]):\n",
    "        xDelta, yDelta = newCP.x - tractCP[v].x, newCP.y - tractCP[v].y\n",
    "        xOFF, yOFF = 0.95 * xDelta, 0.95 * yDelta\n",
    "        vtdGeom[v] = translate(vtdGeom[v],xoff= xOFF, yoff=yOFF)\n",
    "        nTranslated +=1\n",
    "print(\"I translated\",nTranslated,\"vtds toward the center of county\",c)\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]\n",
    "countyGeom[c] = vtdGeom[countyTractList[c][0]]\n",
    "for v in countyTractList[c]:\n",
    "    countyGeom[c] = countyGeom[c].union(vtdGeom[v])\n",
    "    plotPoly(hdCP[v].buffer(0.03))\n",
    "plotPoly(countyGeom[c])\n",
    "unsquishedMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "#plotPoly(MAP)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "5dc3a1ca-5aa2-47f9-9685-83a516dcd1fc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "popHDlist = populatedTractList.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "ecc512e1-cb48-4c0b-90f4-b90458ee23f6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here we compute the map's convex hull.  And here is a dot map of the (shifted) unit centerpoints\n",
      "working on HD center 8\n",
      "working on HD center 765\n",
      "working on HD center 1532\n",
      "working on HD center 2250\n",
      "working on HD center 2752\n",
      "working on HD center 3252\n",
      "working on HD center 3754\n",
      "working on HD center 4254\n",
      "working on HD center 4755\n",
      "working on HD center 5278\n",
      "working on HD center 5778\n",
      "working on HD center 6281\n",
      "working on HD center 6781\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note the MAP convex hull.  Overwrite with your own polygon if desired.\n"
     ]
    }
   ],
   "source": [
    "#OPTION 2 - draw polygonal HDs.  #REQUIRES CONSISTENT TREATMENT OF CCB'S  #For FL, we leave CCB's in the map, draw ALL vtds\n",
    "#Can draw for each vtd.  Or, read in tract or blockgroup geometries, use those pops and unit centerpoints\n",
    "print(\"Here we compute the map's convex hull.  And here is a dot map of the (shifted) unit centerpoints\")\n",
    "convexMAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    convexMAP = convexMAP.union(countyGeom[c])\n",
    "#convexMAP = MAP.centroid\n",
    "#for c in range(nCounties):\n",
    "#    if c not in allFusedCounties:\n",
    "#        convexMAP = convexMAP.union(countyGeom[c])\n",
    "MAPhull = convexMAP.convex_hull.buffer(0.01*convexMAP.area**0.5)\n",
    "plotPoly(MAPhull)\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD center\",t)\n",
    "    if hdCP[t].disjoint(convexMAP.convex_hull):\n",
    "        plotPoly(hdCP[t].buffer(0.03))\n",
    "        hdCP[t] = nearest_points(convexMAP.convex_hull,hdCP[t])[0]\n",
    "        plotCenter(\"m\",hdCP[t])\n",
    "    else:\n",
    "        plotPoly(hdCP[t].buffer(0.01))\n",
    "plotPoly(convexMAP)\n",
    "plotPoly(convexMAP.convex_hull)\n",
    "plotPoly(MAPhull)\n",
    "for c in allFusedCounties:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "plt.show()\n",
    "\n",
    "print(\"Note the MAP convex hull.  Overwrite with your own polygon if desired.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "650ae996-d3ca-422f-8ab0-3ffa3cf6eb67",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "nHDs = nTracts  #need to run this if we start option 2 from a vest list, not a previously run HD poly list\n",
    "HDcountyNo = countyNo.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "54f09a52-c279-4647-9860-0729d1912b4e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let us establish the (post-squish) point-point distances for all units, about 8 min for 4000-unit state\n",
      "working on unit-unit distances for unit 8 out of 7211 . Time = 0\n",
      "working on unit-unit distances for unit 286 out of 7211 . Time = 45\n",
      "working on unit-unit distances for unit 665 out of 7211 . Time = 90\n",
      "working on unit-unit distances for unit 865 out of 7211 . Time = 138\n",
      "working on unit-unit distances for unit 1332 out of 7211 . Time = 185\n",
      "working on unit-unit distances for unit 1532 out of 7211 . Time = 226\n",
      "working on unit-unit distances for unit 1949 out of 7211 . Time = 261\n",
      "working on unit-unit distances for unit 2149 out of 7211 . Time = 304\n",
      "working on unit-unit distances for unit 2350 out of 7211 . Time = 341\n",
      "working on unit-unit distances for unit 2551 out of 7211 . Time = 377\n",
      "working on unit-unit distances for unit 2752 out of 7211 . Time = 415\n",
      "working on unit-unit distances for unit 2952 out of 7211 . Time = 449\n",
      "working on unit-unit distances for unit 3152 out of 7211 . Time = 484\n",
      "working on unit-unit distances for unit 3353 out of 7211 . Time = 518\n",
      "working on unit-unit distances for unit 3554 out of 7211 . Time = 550\n",
      "working on unit-unit distances for unit 3754 out of 7211 . Time = 582\n",
      "working on unit-unit distances for unit 3954 out of 7211 . Time = 610\n",
      "working on unit-unit distances for unit 4154 out of 7211 . Time = 636\n",
      "working on unit-unit distances for unit 4354 out of 7211 . Time = 658\n",
      "working on unit-unit distances for unit 4555 out of 7211 . Time = 675\n",
      "working on unit-unit distances for unit 4755 out of 7211 . Time = 696\n",
      "working on unit-unit distances for unit 4978 out of 7211 . Time = 715\n",
      "working on unit-unit distances for unit 5178 out of 7211 . Time = 732\n",
      "working on unit-unit distances for unit 5378 out of 7211 . Time = 748\n",
      "working on unit-unit distances for unit 5578 out of 7211 . Time = 762\n",
      "working on unit-unit distances for unit 5778 out of 7211 . Time = 774\n",
      "working on unit-unit distances for unit 5980 out of 7211 . Time = 785\n",
      "working on unit-unit distances for unit 6181 out of 7211 . Time = 794\n",
      "working on unit-unit distances for unit 6381 out of 7211 . Time = 802\n",
      "working on unit-unit distances for unit 6581 out of 7211 . Time = 808\n",
      "working on unit-unit distances for unit 6781 out of 7211 . Time = 812\n",
      "working on unit-unit distances for unit 6981 out of 7211 . Time = 815\n",
      "working on unit-unit distances for unit 7181 out of 7211 . Time = 816\n",
      "All done; total elapsed  816 seconds for FL with 26 districts to map\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 ....... ##########  THIS IS FOR TRACT - TRACT; SEE LATER FOR unit - unit ...\n",
    "print(\"Let us establish the (post-squish) point-point distances for all units, about 8 min for 4000-unit state\")\n",
    "#NOTE: FOR LARGE STATES, RESTRICT THE SEARCH TO COUNTIES WITHIN A 3/SQRT(nDistrict) DIAMETER OF THE HOME UNIT\n",
    "uuDist = [[int(maxD+1) for t in range(nHDs)] for t in range(nHDs)] #default = too big to capture\n",
    "closeCountyList = [list() for c in range(nCounties)]\n",
    "closeCountyDist = maxD   #min(maxD,3./float(nDistricts)**0.5 * maxD )  #use above maxD for these counties as well\n",
    "xScaleSquared = xScale*xScale\n",
    "if nDistricts < 10: #closeCountyDist >= maxD:  #small state\n",
    "    closeCountyList = [[i for i in range(nCounties)] for c in range(nCounties)]  #all counties are close enough\n",
    "else:\n",
    "    for c in uncutCountyList:\n",
    "        closeCountyList[c].append(c)\n",
    "        for cc in range(c+1,nCounties):\n",
    "            if cc in uncutCountyList:\n",
    "                if cc in neighborCountyList[c]:\n",
    "                    closeCountyList[c].append(cc)\n",
    "                    closeCountyList[cc].append(c)\n",
    "                else:    \n",
    "                    dist = getLongDist(countyCP[c], countyCP[cc],xScale)\n",
    "                    if dist < closeCountyDist:\n",
    "                        closeCountyList[c].append(cc)\n",
    "                        closeCountyList[cc].append(c)\n",
    "startTime = time.time()\n",
    "#populatedTractList = popHDlist.copy()  #nomenclature\n",
    "for i,t in enumerate(populatedTractList):\n",
    "    if i %200 == 0:\n",
    "        print(\"working on unit-unit distances for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime))\n",
    "    uuDist[t][t] == 0.\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "                dist = getLongDist(hdCP[t], hdCP[tt],xScale)\n",
    "                uuDist[t][tt], uuDist[tt][t] = dist, dist\n",
    "print(\"All done; total elapsed \",int(time.time()-startTime),\"seconds for\",STATE,\"with\",nDistricts,\"districts to map\" )\n",
    "plt.hist(uuDist)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "bc91a02a-52b2-4ac3-afc2-7f1650e32922",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similar to above, but for angles\n",
      "working on unit-unit angles for unit 8 out of 7211 . Time = 0\n",
      "working on unit-unit angles for unit 286 out of 7211 . Time = 24\n",
      "working on unit-unit angles for unit 665 out of 7211 . Time = 47\n",
      "working on unit-unit angles for unit 865 out of 7211 . Time = 73\n",
      "working on unit-unit angles for unit 1332 out of 7211 . Time = 98\n",
      "working on unit-unit angles for unit 1532 out of 7211 . Time = 122\n",
      "working on unit-unit angles for unit 1949 out of 7211 . Time = 141\n",
      "working on unit-unit angles for unit 2149 out of 7211 . Time = 164\n",
      "working on unit-unit angles for unit 2350 out of 7211 . Time = 185\n",
      "working on unit-unit angles for unit 2551 out of 7211 . Time = 205\n",
      "working on unit-unit angles for unit 2752 out of 7211 . Time = 223\n",
      "working on unit-unit angles for unit 2952 out of 7211 . Time = 241\n",
      "working on unit-unit angles for unit 3152 out of 7211 . Time = 260\n",
      "working on unit-unit angles for unit 3353 out of 7211 . Time = 278\n",
      "working on unit-unit angles for unit 3554 out of 7211 . Time = 293\n",
      "working on unit-unit angles for unit 3754 out of 7211 . Time = 309\n",
      "working on unit-unit angles for unit 3954 out of 7211 . Time = 324\n",
      "working on unit-unit angles for unit 4154 out of 7211 . Time = 337\n",
      "working on unit-unit angles for unit 4354 out of 7211 . Time = 350\n",
      "working on unit-unit angles for unit 4555 out of 7211 . Time = 361\n",
      "working on unit-unit angles for unit 4755 out of 7211 . Time = 374\n",
      "working on unit-unit angles for unit 4978 out of 7211 . Time = 384\n",
      "working on unit-unit angles for unit 5178 out of 7211 . Time = 393\n",
      "working on unit-unit angles for unit 5378 out of 7211 . Time = 402\n",
      "working on unit-unit angles for unit 5578 out of 7211 . Time = 411\n",
      "working on unit-unit angles for unit 5778 out of 7211 . Time = 417\n",
      "working on unit-unit angles for unit 5980 out of 7211 . Time = 423\n",
      "working on unit-unit angles for unit 6181 out of 7211 . Time = 428\n",
      "working on unit-unit angles for unit 6381 out of 7211 . Time = 433\n",
      "working on unit-unit angles for unit 6581 out of 7211 . Time = 436\n",
      "working on unit-unit angles for unit 6781 out of 7211 . Time = 438\n",
      "working on unit-unit angles for unit 6981 out of 7211 . Time = 440\n",
      "working on unit-unit angles for unit 7181 out of 7211 . Time = 440\n",
      "All done with angles; total elapsed seconds = 440\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 \n",
    "print(\"similar to above, but for angles\")  #convention is angle[a][b] is from a to b\n",
    "uuAngle = [[0. for t in range(nHDs)] for t in range(nHDs)]\n",
    "\n",
    "pi = 3.141592653\n",
    "twoPi = pi*2.\n",
    "startTime = time.time()\n",
    "for i, t in enumerate(populatedTractList):\n",
    "    if i %200 == 0:\n",
    "        print(\"working on unit-unit angles for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime)  )\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "            angLE = math.atan2(hdCP[tt].y - hdCP[t].y, xScale * (hdCP[tt].x - hdCP[t].x) )\n",
    "            uuAngle[t][tt], uuAngle[tt][t] = angLE % twoPi, (angLE + pi) % twoPi\n",
    "print(\"All done with angles; total elapsed seconds =\",int(time.time()-startTime) )\n",
    "plt.hist(uuAngle)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "4e4690c9-a317-4944-b5fa-8ba6af7c26e9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20003471.0 20003471 1.0\n"
     ]
    }
   ],
   "source": [
    "HDweight = [0 for t in range(nHDs)]\n",
    "for t in populatedTractList:\n",
    "    HDweight[t] = tractPop[t] / statePop #skipping the cutList tracts\n",
    "print(statePop,np.sum([tractPop[t] for t in populatedTractList]), np.sum(HDweight)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "3214fdc5-d104-4164-9478-7ab097ce01e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\n",
      "For each Home District, we will consider a wedge as one-from-full when it is within 2492.6443613707165 of targetWedgePop\n",
      "I am working on HD number 8 of 7211 HDs.  Elapsed sec = 0\n",
      "I am working on HD number 665 of 7211 HDs.  Elapsed sec = 79\n",
      "I am working on HD number 1332 of 7211 HDs.  Elapsed sec = 141\n",
      "I am working on HD number 1949 of 7211 HDs.  Elapsed sec = 219\n",
      "I am working on HD number 2350 of 7211 HDs.  Elapsed sec = 304\n",
      "I am working on HD number 2752 of 7211 HDs.  Elapsed sec = 381\n",
      "I am working on HD number 3152 of 7211 HDs.  Elapsed sec = 446\n",
      "I am working on HD number 3554 of 7211 HDs.  Elapsed sec = 526\n",
      "I am working on HD number 3954 of 7211 HDs.  Elapsed sec = 591\n",
      "I am working on HD number 4354 of 7211 HDs.  Elapsed sec = 651\n",
      "I am working on HD number 4755 of 7211 HDs.  Elapsed sec = 734\n",
      "I am working on HD number 5178 of 7211 HDs.  Elapsed sec = 812\n",
      "I am working on HD number 5578 of 7211 HDs.  Elapsed sec = 877\n",
      "I am working on HD number 5980 of 7211 HDs.  Elapsed sec = 943\n",
      "I am working on HD number 6381 of 7211 HDs.  Elapsed sec = 1027\n",
      "I am working on HD number 6781 of 7211 HDs.  Elapsed sec = 1123\n",
      "I am working on HD number 7181 of 7211 HDs.  Elapsed sec = 1215\n",
      "1218.689 seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of unit usage.\n",
      "unit avg and sd usage are 1.00019 0.16716\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 .......\n",
    "# BELOW modified Dec23 to use inter-unit distances and angles, (counties still wedgeIntersxn) otherwise similar to HD2\n",
    "# --THIS ESSENTIALLY TAKES THE ORIGINAL TRACT-BASED HD centerpoints, and redraws HD2 districts after convexifying corners and convex_hull\n",
    "print(\"Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\") \n",
    "#this is a hybrid of HD2 and the organic code, in that all wedge tracts must be in a chain of neighboring counties\n",
    "#General sequence: draw 4 equi-angle wedges with random starting orientation.  If not all can fill a quarter aDP ...\n",
    "# ... find the closest map boundary point to the tract center point in the shortest wedge.  Orient the 0th wedge to face this point\n",
    "# ... determine the \"maxWedgePop\" for this short wedge and the other three wedges extended past the boundary\n",
    "# ...  if this results in only one short wedge, or opposing short wedges, adjust wedge angles\n",
    "# Regardless of whether we re-oriented or adjusted angles, compute each wedgePop for infinite radius\n",
    "#   ... then adjust other targetWedgePops if some found to be short.\n",
    "#   ... for non-shorted wedges, use bisection method to adjust radius to meet targetWedgePop\n",
    "#   ... once total HDpop within tolerance, add/subtract a tract fraction to fulfill HDpop\n",
    "#  create a list of fully used tracts per HD, save the tract# of the split tract and its fractional use\n",
    "#  Compute tractUse across all HDs at the end from the tractUsageLists.  Defer patching and partisan calcs to another code\n",
    "\n",
    "pi=3.1415926536\n",
    "#MAPhull = MAP.convex_hull #used for exitAngle calc.  Defined in an above block to allow user modification\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2.*pi / nWedges\n",
    "angle, angle2, wedgePoly = [0.]*nWedges, [0.]*nWedges, [dummyPoly]*nWedges  #start and stop angle of each wedge\n",
    "pt1, pt2, pt3 = [Point(0,0)]*nWedges, [Point(0,0)]*nWedges, [Point(0,0)]*nWedges #these four define the polygon wedge for a growing home district\n",
    "tractUse = [0.] * nHDs  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "HDtractList = [list()]*nHDs\n",
    "splitTractNo = [-999]*nHDs  #the tract that is only partially used to finish each HD\n",
    "splitTractUse = [0.]*nHDs  #and this is what fraction of the split tract is included\n",
    "HDvPop = [0.]*nHDs\n",
    "#HDweight = [tractPop[t] / (statePop - excludedPop) for t in range(nTracts)]  #relative weight of each drawn HD\n",
    "HDPoly1 = [dummyPoly]*nHDs  #final 4-wedge shape, unionized from blockgroups / tracts\n",
    "HDarea = [0.]*nHDs  #geographical area of each home district (after boundary trim)\n",
    "HDradius = [[0.]*nWedges for t in range(nHDs)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nHDs)] #final starting angle for each HD wedge\n",
    "angle0 = [-999.] * nHDs  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "avgTractPop = statePop/len(populatedTractList)\n",
    "tolerPops = [0.8 * avgTractPop, 0.5 * np.max(tractPop)]  #threshold gap before adding one more unit to a wedge \n",
    "tolerPop = tolerPops[0]\n",
    "print(\"For each Home District, we will consider a wedge as one-from-full when it is within\",tolerPops[0],\"of targetWedgePop\")\n",
    "tractPrintInterval = 400  #for tracking progress\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #HD2\n",
    "maxAngle = 1.8*pi/2. # limit to avoid wedges too close to half a pie\n",
    "minAdjRatio = 0.5  #min ratio of pop of adjacent wedge (to min wedge) to targetWedgePop to not consider this a corner HD\n",
    "maxUncap = 0.5 #the max ratio of uncaptured pop in a maxWedge vs. target wedge pop that we will not stretch to include (7/23)\n",
    "oppWt = 0.0    #the fraction of gap between normal targetWedgePop and the minWedgePop that we allow the opposite wedge to add to tgtPop = minWedgePop\n",
    "maxWedgePop = [0.] * nWedges  #wedge pop if we explode wedge to maximum radius\n",
    "printDebug = \"no\"\n",
    "codeStartTime = time.time()\n",
    "hdCPpop = [HDweight[t] * statePop for t in range(nHDs)]\n",
    "countyHDlist = [list() for c in range(nCounties)]\n",
    "for t in populatedTractList:\n",
    "    countyHDlist[HDcountyNo[t]].append(t)\n",
    "\n",
    "for kkkj, t in enumerate(populatedTractList):  #range(nTracts) : #loop on each tract. \n",
    "    hC = HDcountyNo[t]     #this tract's home county\n",
    "    HDtractList[t] = [t] #seed the list of tracts in this HD\n",
    "    wedgeList = [list() for w in range(nWedges)]   #list of each wedge's tracts, excluding home tract\n",
    "    barredList = list()  #list of wedge numbers for which we are barred from altering their targetWedgePops\n",
    "    if (kkkj % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on HD number {0} of {1} HDs.  Elapsed sec = {2}\".format(t,nHDs,int(time.time()-codeStartTime) ) )  \n",
    "    nActiveWedges = nWedges #active wedges have not run over boundary\n",
    "    wedgePop = [0.]*nWedges\n",
    "    targetWedgePop = [aDP / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    \n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to the midangle of our starting wedge\n",
    "    wedgeAngle = [avgWedgeAngle for w in range(nWedges) ] #reset to equi-angle when starting each tract\n",
    "    for nW in range(nWedges-1):\n",
    "        wedgeAngle[nW] += random.uniform(-0.0001,0.0001)  #this wiggle solves a later indexing ambiguity for corner tracts\n",
    "    wedgeAngle[nWedges-1] = 2.*pi - (np.sum(wedgeAngle) - wedgeAngle[nWedges-1] ) #squaring up to 2pi total\n",
    "    startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "    for nW in range(1,nWedges):\n",
    "        startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "    endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "    #  TRIAL LOOP TO SEE WHICH WEDGES CAN FILL TO TARGET POP w/equiangle wedges\n",
    "    maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]    \n",
    "    maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "    willFill = [0] * nWedges #would this wedge meet its target with infinite radius?\n",
    "    for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "        iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "        #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList) \n",
    "        nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                neighborCountyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "        maxWedgePop[nW] += (iCP + nonCP)\n",
    "        wedgeList[nW] = Li + Ln\n",
    "        if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "            willFill[nW] = 1\n",
    "     \n",
    "    if np.sum(willFill) < nWedges:  #at least one wedge couldn't reach its wedgePop target (went over boundary) ...\n",
    "        minW = maxWedgePop.index(np.min(maxWedgePop)) #.. so we will orient the 0th wedge to face the shortest wedgePop's closest boundary\n",
    "        exitAngle = getExitAngle(hdCP[t],maxWedgePoly[minW], MAPhull, startAngle[minW], endAngle[minW])\n",
    "        angle0[t] = exitAngle - 0.5*(2.*pi / nWedges)  #Now reset all angles based on new starting orientation.  All wedge angles still equal\n",
    "        startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "        for nW in range(1,nWedges):\n",
    "            startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "        endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "        maxWedgePoly = [buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]\n",
    "        \n",
    "        willFill = [0]*nWedges\n",
    "        #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation (wedge angles still constant)\n",
    "        maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop        \n",
    "        for nW in range(nWedges):   #... then add in-county and nonCounty wedge Pop from contiguous chain of counties\n",
    "            iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "            #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "            nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                    neighborCountyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "            maxWedgePop[nW] += (iCP + nonCP)\n",
    "            wedgeList[nW] = Li + Ln\n",
    "            if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                willFill[nW] = 1       \n",
    "    \n",
    "    if np.sum(willFill) < nWedges:  #check if we need to modify wedge angles after possible re-orientation.  \n",
    "        # If so, re-draw maxWedgePoly's, re-calc maxWedgePops\n",
    "        nUnfilledWedges = nWedges - np.sum(willFill)\n",
    "        isChange, newInclA = getNewAngles(maxWedgePop, targetWedgePop, aDP, levelL, minAdjRatio, maxAngle, maxAngleRatio, nUnfilledWedges )\n",
    "        if isChange: #no longer equi-angle\n",
    "            newStartAngle = [0.5*(startAngle[nW]+endAngle[nW]) - 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            newEndAngle =   [0.5*(startAngle[nW]+endAngle[nW]) + 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            startAngle, endAngle, wedgeAngle = newStartAngle.copy(), newEndAngle.copy(), newInclA.copy()\n",
    "            maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], \n",
    "                                        maxD/math.cos(0.5*wedgeAngle[nW]), xScale ) for nW in range(nWedges) ]\n",
    "            willFill = [0]*nWedges\n",
    "            #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation and new wedge angles\n",
    "            maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "            for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "                iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])                \n",
    "                #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "                nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                        neighborCountyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "                maxWedgePop[nW] += (iCP + nonCP)\n",
    "                wedgeList[nW] = Li + Ln\n",
    "            minW = wedgeAngle.index(np.max(wedgeAngle)) #should be 0th wedge, but playing it safe.  This is the 1st wide-angle wedge ..\n",
    "            oppW = int(int(minW+nWedges/2)%nWedges)   #and its opposite\n",
    "            if maxWedgePop[minW] > maxWedgePop[oppW] and maxWedgePop[oppW] < aDP / nWedges:  #minW and oppW are flipped after inclA adjmt\n",
    "                oppW = minW\n",
    "                minW = int(int(minW+nWedges/2)%nWedges)\n",
    "            if maxWedgePop[minW] < targetWedgePop[oppW]:  #we need to constrain oppW in this HD2 implementation; redistribute tgt to adjacents\n",
    "                maxNonOppW = np.sum(maxWedgePop) - maxWedgePop[oppW] #...but only if other wedges can pick up slack; need to check\n",
    "                minOppWedgePop = aDP - maxNonOppW  #cannot set oppW target below this or we won't reach aDP across all wedges\n",
    "                barredList = [oppW]\n",
    "                targetWedgePop[oppW] = max(minOppWedgePop, maxWedgePop[minW] + oppWt * (targetWedgePop[oppW] - maxWedgePop[minW]) )\n",
    "                tWPgap = aDP / float(nWedges) - targetWedgePop[oppW] \n",
    "                for nW in range(nWedges):\n",
    "                    if nW != minW and nW != oppW:\n",
    "                        targetWedgePop[nW] += tWPgap/float(nWedges - 2.)  #if oppW target is limited, allot to adjacent wedges\n",
    "            for nW in range(nWedges):\n",
    "                if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                    willFill[nW] = 1  \n",
    "    \n",
    "    if abs(np.sum(targetWedgePop) / aDP - 1.) > 0.001:\n",
    "        raise Exception(\"FAIL! Our target wedgepops\",targetWedgePop, np.sum(targetWedgePop),\"no longer add up to\",aDP)\n",
    "    \n",
    "    if np.sum(willFill) == nWedges:  #all wedges will fill.  Will any leave a small near-boundary remnant?  If so, pick up smallest remnant\n",
    "        remnantWedgePopFrac = [ ( maxWedgePop[w] - targetWedgePop[w] )/targetWedgePop[w] for w in range(nWedges) ]\n",
    "        if np.min(remnantWedgePopFrac) < maxUncap :  #lessen tWP's of *adjacent* wedges, then increase this wedge's target --> max\n",
    "            minW = remnantWedgePopFrac.index(np.min(remnantWedgePopFrac))\n",
    "            oppW = int((minW+nWedges/2)%nWedges)\n",
    "            #print(\"filling to boundary for tract,wedge, tWP, mWP =\",t,minW, r3(targetWedgePop[minW]), maxWedgePop[minW])\n",
    "            for nW in range(nWedges):\n",
    "                if nW != minW and nW != oppW:\n",
    "                    targetWedgePop[nW] -= (remnantWedgePopFrac[minW] * targetWedgePop[minW] ) / (nWedges - 2.)\n",
    "            targetWedgePop[minW] = maxWedgePop[minW]\n",
    "            #       OK, READY TO SOLVE FOR NON-FILLED WEDGE SIZES once we adjust targets for unfilled wedges\n",
    "    #print(t,\" prior to rebalanceTWP call, mWPs, tWPs are\",maxWedgePop, targetWedgePop)\n",
    "    targetWedgePop, willFill = rebalanceTWPs(maxWedgePop, targetWedgePop, barredList)\n",
    "    #if np.max(targetWedgePop) - np.min(targetWedgePop) > 0.02 * aDP: #debug\n",
    "    #    print(\"for tract\",t,\",  After rebalancing but before tweaks for blocky pop adds: willFill, targetWedgePops and maxWedgePops are ...\")\n",
    "    #    for w in range(nWedges):\n",
    "    #        print(w, willFill[w],r3(targetWedgePop[w]),int(maxWedgePop[w]) )\n",
    "    for w in range(nWedges):  #WHEW!  WE CAN FINALLY ASSIGN ALL WEDGES' POPS AND TRACTLISTS\n",
    "        loop = 0\n",
    "        if willFill[w] == 0:  #wedge is maxed out\n",
    "            wedgePop[w] = maxWedgePop[w]\n",
    "            #wedgeList[w] = ...  #we already captured this list = maxWedge list\n",
    "            HDradius[t][w] = maxD/math.cos(0.5*wedgeAngle[w])\n",
    "        else:  #need to solve for wedge radius to meet targetPop.  Use bisection as scipy minimize no faster\n",
    "            HDcpWP = hdCPpop[t] * wedgeAngle[w]/(2.*pi)\n",
    "            nonHDcpTWP = targetWedgePop[w] - HDcpWP\n",
    "            #wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeB(nontractTWP,t,hC,maxWedgePoly[w],tolerPop,tractCP, tractPop,\n",
    "            #                                                        countyNo,countyTractList,countyGeom, neighborCountyList,uuDist[t])\n",
    "            wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeC(nonHDcpTWP,t,hC, maxWedgePoly[w],startAngle[w], endAngle[w], tolerPop,\n",
    "                                                                    hdCP, hdCPpop,HDcountyNo, countyHDlist,countyGeom,\n",
    "                                                                    neighborCountyList,uuDist[t],uuAngle[t])\n",
    "            popGap = nonHDcpTWP - wedgePop[w]  #gap between target and solved wedge pop; can be negative\n",
    "            notYetAdjusted, nextW = True, w+1  #looking to adjust a later wedge's target pop to minimize drift in total HD pop\n",
    "            while notYetAdjusted and nextW < nWedges:\n",
    "                if willFill[nextW] == 1 and maxWedgePop[nextW] > targetWedgePop[nextW] + popGap :  #can accommodate\n",
    "                    targetWedgePop[nextW] += popGap\n",
    "                    notYetAdjusted = False\n",
    "                nextW +=1          \n",
    "\n",
    "        HDangle[t][w] = startAngle[w]\n",
    "    #print(t,\"tract. After TWP adjustment, targetWedgePops are\",targetWedgePop)\n",
    "    HDtractList[t] = [t]\n",
    "    totPop = hdCPpop[t]\n",
    "    for nW in range(nWedges):\n",
    "        for tt in wedgeList[nW]:\n",
    "            HDtractList[t].append(tt)  #building the list of tracts in the Home District  (we'll keep up with below changes)\n",
    "            totPop += hdCPpop[tt]\n",
    "    offsetPop = totPop - aDP  #we have solved for all wedge radii to get each within one tractPop of target ... \n",
    "    HDvPop[t] = totPop\n",
    "    #if offsetPop > 0:   #... now include a splitPoly to nail the avg District Pop\n",
    "    #    isOver = True  #we slightly overshot.  ID the farthest-away tract for split-jettison\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], hdCPpop, neighborList)\n",
    "    #    HDvPop[t] = totPop - (1. - splitTractUse[t]) * tractPop[splitTractNo[t]]\n",
    "    #if offsetPop < 0:\n",
    "    #    isOver = False  #we have undershot.  Pick up the nearest contiguous tract that can be split to fill the gap\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], tractPop, neighborList)\n",
    "    #    HDtractList[t].append(splitTractNo[t])\n",
    "    #    HDvPop[t] = totPop + splitTractUse[t]*tractPop[splitTractNo[t]]       \n",
    "    #if abs(1. - HDvPop[t]/aDP) > 0.005 :  #something went wrong with pop assignation for this HD\n",
    "    #    print(\"WARNING! Total Home District pop was\",HDvPop[t],\"vs target\",aDP,\"for tract\",t)   ##### hdCP topology not yet known; skip partial\n",
    "        \n",
    "    HDPoly1[t] = dummyPoly  #tractGeom[t] #skip constructing the HD poly shape.  Do compute area of the Home District, including final partial\n",
    "    HDarea[t] = 0.\n",
    "    for tt in HDtractList[t]:\n",
    "        if tt == splitTractNo[t]:\n",
    "            tractUse[tt] += nDistricts * HDweight[t] * splitTractUse[t] \n",
    "            #HDarea[t]   += tractArea[tt] * splitTractUse[t]  #these are original (nonsquished) areas\n",
    "            \n",
    "        else:\n",
    "            tractUse[tt] += nDistricts * HDweight[t]\n",
    "            #HDarea[t]    += tractArea[tt]\n",
    "            #HDPoly1[t] = HDPoly1[t].union(tractGeom[tt])\n",
    "    HDPoly1[t] = buildPoly(hdCP[t],HDradius[t], HDangle[t] )\n",
    "    #HDarea[t] = HDPoly1[t].area + splitTractUse[t] * tractArea[splitTractNo[t]]  \n",
    "    #if splitTractUse[t] > 0.5:\n",
    "    #    HDPoly1[t] = HDPoly1[t].union(tractGeom[splitTractNo[t]])\n",
    "                          \n",
    "    #ALL DONE ESTABLISHING THE FINAL HDTRACTLIST.  FINALIZE STATS\n",
    "totalTime = time.time() - codeStartTime\n",
    "print(r3(totalTime),\"seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of unit usage.\")\n",
    "n_bins=50\n",
    "popTractUse, plotWts = [tractUse[t] for t in populatedTractList], [HDweight[t] for t in populatedTractList]\n",
    "\n",
    "origHDuseAvg, origHDuseSD = getWeightedAvgAndSD(tractUse,HDweight)\n",
    "print(\"unit avg and sd usage are\",r5(origHDuseAvg), r5(origHDuseSD) )\n",
    "\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "ax.hist(popTractUse, bins=n_bins, weights=plotWts)\n",
    "plt.show()\n",
    "HDvPop = [np.sum([hdCPpop[tt] for tt in HDtractList[t]]) for t in range(nHDs)]\n",
    "plt.scatter([hdCPpop[t] for t in populatedTractList],[HDvPop[t] for t in populatedTractList] )\n",
    "print(\"here are your HD pops\")\n",
    "plt.show()\n",
    "origHDHDtractList = [HDtractList[t].copy() for t in range(nHDs)] #save for posterity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "aec0cf55-112b-4eba-93c6-9cf1d1b87aae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we will write the polygon-based results to a file\n"
     ]
    }
   ],
   "source": [
    "#last block of option 2 (i.e. generating polygonal HDs from scratch)\n",
    "print(\"Now we will write the polygon-based results to a file\")\n",
    "tractNo = [t for t in range(nHDs) ]\n",
    "HDangle0 = [HDangle[t][0] for t in range(nHDs) ]\n",
    "HDangle1 = [HDangle[t][1] for t in range(nHDs) ]\n",
    "HDangle2 = [HDangle[t][2] for t in range(nHDs) ]\n",
    "HDangle3 = [HDangle[t][3] for t in range(nHDs) ]\n",
    "HDradius0 = [HDradius[t][0] for t in range(nHDs)]\n",
    "HDradius1 = [HDradius[t][1] for t in range(nHDs)]\n",
    "HDradius2 = [HDradius[t][2] for t in range(nHDs)]\n",
    "HDradius3 = [HDradius[t][3] for t in range(nHDs)]\n",
    "hdCPx, hdCPy =   [hdCP[t].x for t in range(nHDs)], [hdCP[t].y for t in range(nHDs)]\n",
    "\n",
    "\n",
    "paramList = [\"STATE\",\"statePop\",\"nDistricts\",\"nHDs\",\"nWedges\",\"popn-toler\",\"levelL\", \"maxAngleRatio\",\n",
    "             \"maxAngle\",\"minAdjRatio\",\"maxUncap\", \"oppWt\", \"calc sec\",\"xScale\",\"outputs...\"]\n",
    "paramValues = [STATE,statePop, nDistricts, nHDs, nWedges, tolerPop, levelL, maxAngleRatio, maxAngle, minAdjRatio, maxUncap,\n",
    "               oppWt, totalTime, xScale, -777]\n",
    "for i in range(nHDs-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "HDdf = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"countyNo\":HDcountyNo,\n",
    "                    \"tractNo\":tractNo,\"centroid x\":hdCPx,\"centroid y\":hdCPy,\"tractPop\":hdCPpop,\n",
    "                    \"HDweight\":HDweight,\"HD-pop\":HDvPop,\"HDarea\":HDarea,\n",
    "                    \"startAngle\":angle0,\"HDangle0\":HDangle0,\"HDangle1\":HDangle1,\"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\n",
    "                    \"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\"HDradius2\":HDradius2, \"HDradius3\":HDradius3,\"tractUse\":tractUse,\n",
    "                    \"splitTractNo\":splitTractNo,\"splitTractUse\":splitTractUse,\"HDtractList\":HDtractList} ) \n",
    "\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Bnopatch\"+str(int(nDistricts))+\"nW4.csv\"  #solveWedgeB\n",
    "outname = STATE+str(int(nHDs))+\"convexHD2_\"+str(int(nDistricts))+\"nW4_03Mar.csv\"  #solveWedgeC\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Buutpatch\"+str(int(nDistricts))+\"nW4.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "HDdf.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b7a38aff-e4c4-498a-9f63-b6d5d02ab158",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999998006\n"
     ]
    }
   ],
   "source": [
    "print(np.average(HDweight) * nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "253554da-8842-470e-b9b3-5626823059b0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's ensure normalization of each cluster's usage based on how much of its area must be captured to consider a cluster captured\n",
      "working on cluster no 0 containing counties [43]\n",
      "halfway there! last use was 0.62785\n",
      "our final fractional capture area to normalize this cluster's usage is 0.26146\n",
      "working on cluster no 1 containing counties [44]\n",
      "halfway there! last use was 0.79483\n",
      "our final fractional capture area to normalize this cluster's usage is 0.22671\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#skip this block, use one below if I already CREATED HDVTDLIST'S FROM POLYGONS and there are non-vtd units but all tracts are vtds\n",
    "print(\"Let's ensure normalization of each cluster's usage based on how much of its area must be captured to consider a cluster captured\")\n",
    "#origHDlist = [list() for t in range(nHDs)],\n",
    "splitTractNo, splitTractUse = [-999]*nHDs, [0.]*nHDs\n",
    "origHDpop = [0. for t in range(nHDs)]  #these will be based on captured Census vtd's\n",
    "HDpoly, HDdiam = [dummyPoly for t in range(nHDs)], [0. for t in range(nHDs)]\n",
    "\n",
    "popHDlist = list()  #rebuild here again with the geometries\n",
    "for t in range(nHDs):\n",
    "    if HDweight[t] > 0.000001 :  #HD poly was actually drawn  #[(HDradius[t][w]+0.001*maxD) for w in range(4)]\n",
    "        HDpoly[t] = buildPoly(hdCP[t], HDradius[t], HDangle[t])  #expand to catch the units on HD edge\n",
    "        HDdiam[t] = HDpoly[t].area **0.5  #approximate, for later scoring purposes of underuse * distance\n",
    "        popHDlist.append(t)\n",
    "\n",
    "\n",
    "CCBuse, CCBareaFrac, CCBarea = [0. for CCB in CCBlist], [0.5 for CCB in CCBlist], [geo.area for geo in CCBgeom]\n",
    "for j,geo in enumerate(CCBgeom):\n",
    "    print(\"working on cluster no\",j,\"containing counties\",CCBlist[j] )\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    HDfrac = [ 0. for t in range(nHDs) ]\n",
    "    for t in range(nHDs):\n",
    "        if HDcountyNo[t] in CCBlist[j]:\n",
    "            HDfrac[t] = 1.\n",
    "        else:\n",
    "            HDfrac[t] = HDpoly[t].intersection(geo).area / CCBarea[j]\n",
    "    idx = np.argsort(HDfrac)\n",
    "    i = nHDs\n",
    "    while CCBuse[j] < 1.000 - 0.5 * nDistricts*np.average(HDweight) and i > 0:\n",
    "        i -=1\n",
    "        t = idx[i]\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        if CCBuse[j] > 0.5 and CCBuse[j] - HDweight[t] * nDistricts < 0.5:\n",
    "            print(\"halfway there! last use was\",r5(HDfrac[t]))\n",
    "            plotPoly(HDpoly[t],0.2)\n",
    "        #origHDlist[t].append(unitNo)  #postpone this until main loop\n",
    "        #origHDpop[t] += CCBpop[j]     #postpone until main\n",
    "    if i <= 1:\n",
    "        print(\"WARNING, only\",r5(CCBuse[j]),\" of a district's worth of ensemble intersected the cluster geometry\")\n",
    "        # Add more stuff here to inflate the poly and try again ...\n",
    "    else:\n",
    "        CCBareaFrac[j] = HDfrac[t]\n",
    "        print(\"our final fractional capture area to normalize this cluster's usage is\",r5(CCBareaFrac[j]) )\n",
    "        plotPoly(MAP,0.2)\n",
    "        plotPoly(geo,0.8)\n",
    "        plotCenter(j,geo)\n",
    "        plotPoly(HDpoly[t],1.3)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "868ec654-07ad-4925-bea5-105e8fe9bc59",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDtractList = [list(set(oldHDtractList[t] ) ) for t in range(nHDs)] #restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "63418340-35f5-4a32-a0aa-6b04478b3f57",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will now update vtd lists to either fully take or shed CCBs\n",
      "working on cluster no -1 containing counties [43]\n",
      "halfway there! last use was 0.86639\n",
      "our final fractional capture area to normalize this cluster's usage is 0.16293 from 255 intersecting HDs\n",
      "Now I will update vtd lists to move full CCB 0 in or out\n",
      "working on cluster no 0 containing counties [44]\n",
      "halfway there! last use was 0.90534\n",
      "our final fractional capture area to normalize this cluster's usage is 0.31677 from 262 intersecting HDs\n",
      "Now I will update vtd lists to move full CCB 1 in or out\n"
     ]
    }
   ],
   "source": [
    "#new for FL - alt to above, using pop-capture threshold on vtd Lists, not area threshold on HDpoly's for including CCBs\n",
    "oldHDtractList = [HDtractList[t].copy() for t in range(nHDs)] #safekeeping\n",
    "print(\"we will now update vtd lists to either fully take or shed CCBs\")\n",
    "CCBuse, CCBpopFracThreshold = [0. for CCB in CCBlist], [0.0001 for CCB in CCBlist]\n",
    "for j,CCBl in enumerate(CCBlist):\n",
    "    CCBtL = list()  #list of tracts in the CCB\n",
    "    for c in CCBl:\n",
    "        CCBtL += countyTractList[c]\n",
    "    addList, nIntersections = list(), 0\n",
    "    print(\"working on cluster no\",j-1,\"containing counties\",CCBl )\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    HDfrac = [ 0. for t in range(nHDs) ]\n",
    "    for t in popHDlist:\n",
    "        if HDcountyNo[t] in CCBl:\n",
    "            HDfrac[t] = 1.\n",
    "        else:\n",
    "            cPop = 0\n",
    "            for v in CCBtL:\n",
    "                if v in oldHDtractList[t]:\n",
    "                    cPop += tractPop[v]\n",
    "            HDfrac[t] = cPop / CCBpop[j]\n",
    "    idx = np.argsort(HDfrac)\n",
    "    i = nHDs\n",
    "    while CCBuse[j] < 1.000 - 0.5 * nDistricts*np.average(HDweight) and i > 0:  #0.5 ... is to land near avg AFTER last added\n",
    "        i -=1\n",
    "        nIntersections +=1\n",
    "        t = idx[i]\n",
    "        addList.append(t)\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        if CCBuse[j] > 0.5 and CCBuse[j] - HDweight[t] * nDistricts < 0.5:\n",
    "            print(\"halfway there! last use was\",r5(HDfrac[t]))\n",
    "    if i <= 1:\n",
    "        print(\"WARNING, only\",r5(CCBuse[j]),\" of a district's worth of ensemble intersected the cluster geometry\")\n",
    "        # Add more stuff here to inflate the poly and try again ...\n",
    "    else:\n",
    "        CCBpopFracThreshold[j] = HDfrac[t]\n",
    "        print(\"our final fractional capture pop to normalize this cluster's usage is\",r5(CCBpopFracThreshold[j]),\n",
    "              \"from\",nIntersections,\"intersecting HDs\" )\n",
    "    print(\"Now I will update vtd lists to move full CCB\",j,\"in or out\")\n",
    "    for t in popHDlist:\n",
    "        if HDfrac[t] > 0:\n",
    "            HDtractList[t] = list(set(HDtractList[t]).difference(CCBtL))\n",
    "            if t in addList:\n",
    "                HDtractList[t] += CCBtL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "beb8908f-1855-4224-9cbe-6f484a193f5a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDunitList = [origHDlist[t].copy() for t in range(nHDs) ]  #for safekeeping - is this from option 1??\n",
    "HDvPop = origHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61c1f835-ecad-444a-9f0e-0ab5c0e394e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4328e35e-089d-4f81-9987-a2cf0785a4ff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not sure why ./2024state_HD_output/FL7211convexHD2_26nW4.csv isn't giving good polygon-based pops\n",
      "Lets just pull in the HDtractLists from that HD2 code and use them\n",
      "Must have already established unitGeoms, pops, topology\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched vtdlist, e.g. ./2024state_HD_output/FL7211convexHD2_26nW4.csv ./2024state_HD_output/FL7211convexHD2_26nW4.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in 7211 HD lists of vtds\n",
      "working on converting HD 0 from vtd list to unit list\n",
      "working on converting HD 500 from vtd list to unit list\n",
      "working on converting HD 1000 from vtd list to unit list\n",
      "working on converting HD 1500 from vtd list to unit list\n",
      "working on converting HD 2000 from vtd list to unit list\n",
      "working on converting HD 2500 from vtd list to unit list\n",
      "working on converting HD 3000 from vtd list to unit list\n",
      "working on converting HD 3500 from vtd list to unit list\n",
      "working on converting HD 4000 from vtd list to unit list\n",
      "working on converting HD 4500 from vtd list to unit list\n",
      "working on converting HD 5000 from vtd list to unit list\n",
      "working on converting HD 5500 from vtd list to unit list\n",
      "working on converting HD 6000 from vtd list to unit list\n",
      "working on converting HD 6500 from vtd list to unit list\n",
      "working on converting HD 7000 from vtd list to unit list\n",
      "orig unit avg and sd usage are 0.99988 0.16931\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now re-calculate HD pops based on unit assignments\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to correcting for discontiguity and squaring up cluster-unit usage...\n",
      "CCB cluster 0 with pop 82874.0 originally had use of 0.8198743108131852\n",
      "CCB cluster 1 with pop 90352.0 originally had use of 0.8033775738218742\n"
     ]
    }
   ],
   "source": [
    "#ALTERNATE RESTART FROM A DECENT HDtractList  -THIS IS OUR CURRENT FL APPROACH\n",
    "print(\"Not sure why ./2024state_HD_output/FL7211convexHD2_26nW4.csv isn't giving good polygon-based pops\")\n",
    "print(\"Lets just pull in the HDtractLists from that HD2 code and use them\")\n",
    "\n",
    "print(\"Must have already established unitGeoms, pops, topology\")\n",
    "infile = input(\"enter the unpatched vtdlist, e.g. ./2024state_HD_output/FL7211convexHD2_26nW4.csv\")\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDtractList\"] #HDvtdList\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "# HDvPop = inDF[\"HDvPop\"].to_list()  #don't care; will rebuild from units\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of vtds\")\n",
    "HDunitList = [list() for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if t%500 == 0:\n",
    "        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "        if v in allUnits:\n",
    "            HDunitList[t].append(allUnits.index(v))\n",
    "    for c in unitCounties:\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(c+0.5)\n",
    "            HDunitList[t].append(u)\n",
    "    for j, L in enumerate(CCBlist):\n",
    "        c = L[0]\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(j+0.25)\n",
    "            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD\n",
    "\n",
    "print(\"Now re-calculate HD pops based on unit assignments\")\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()        \n",
    "\n",
    "print(\"prior to correcting for discontiguity and squaring up cluster-unit usage...\")\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"with pop\",unitPop[u],\"originally had use of\",unitUse[u])\n",
    "\n",
    "origHDlist   = [HDunitList[t].copy() for t in range(nHDs) ]  #for safekeeping\n",
    "origHDpop =     HDvPop.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "22916ca4-1217-4231-9f11-88110a1a7cda",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to correcting for discontiguity and squaring up cluster-unit usage...\n",
      "CCB cluster 0 with pop 82874.0 originally had use of 1.0065037212789716\n",
      "CCB cluster 1 with pop 90352.0 originally had use of 1.0003609873504258\n"
     ]
    }
   ],
   "source": [
    "print(\"prior to correcting for discontiguity and squaring up cluster-unit usage...\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"with pop\",unitPop[u],\"originally had use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "89cede45-92d7-4bc4-91e9-70b59c61a66b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will now update vtd lists to either fully take or shed CCBs\n",
      "working on cluster no -1 containing counties [43]\n",
      "halfway there! last use was 0.86639\n",
      "our final fractional capture pop to normalize this cluster's usage is 0.16293 from 255 intersecting HDs\n",
      "Now I will update vtd lists to move full CCB 0 in or out\n",
      "working on cluster no 0 containing counties [44]\n",
      "halfway there! last use was 0.90534\n",
      "our final fractional capture pop to normalize this cluster's usage is 0.31677 from 262 intersecting HDs\n",
      "Now I will update vtd lists to move full CCB 1 in or out\n"
     ]
    }
   ],
   "source": [
    "#skip for FL; already squared up\n",
    "oldHDtractList = [inVTDlist[t].copy() for t in range(nHDs)] #safekeeping\n",
    "HDtractList = [inVTDlist[t].copy() for t in range(nHDs)] #nomenclature\n",
    "print(\"we will now update vtd lists to either fully take or shed CCBs\")\n",
    "CCBuse, CCBpopFracThreshold = [0. for CCB in CCBlist], [0.0001 for CCB in CCBlist]\n",
    "for j,CCBl in enumerate(CCBlist):\n",
    "    CCBtL = list()  #list of tracts in the CCB\n",
    "    for c in CCBl:\n",
    "        CCBtL += countyTractList[c]\n",
    "    addList, nIntersections = list(), 0\n",
    "    print(\"working on cluster no\",j-1,\"containing counties\",CCBl )\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    HDfrac = [ 0. for t in range(nHDs) ]\n",
    "    for t in popHDlist:\n",
    "        if HDcountyNo[t] in CCBl:\n",
    "            HDfrac[t] = 1.\n",
    "        else:\n",
    "            cPop = 0\n",
    "            for v in CCBtL:\n",
    "                if v in HDtractList[t]:  #HDtractList\n",
    "                    cPop += tractPop[v]\n",
    "            HDfrac[t] = cPop / CCBpop[j]\n",
    "    idx = np.argsort(HDfrac)\n",
    "    i = nHDs\n",
    "    while CCBuse[j] < 1.000 - 0.5 * nDistricts*np.average(HDweight) and i > 0:  #0.5 ... is to land near avg AFTER last added\n",
    "        i -=1\n",
    "        nIntersections +=1\n",
    "        t = idx[i]\n",
    "        addList.append(t)\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        if CCBuse[j] > 0.5 and CCBuse[j] - HDweight[t] * nDistricts < 0.5:\n",
    "            print(\"halfway there! last use was\",r5(HDfrac[t]))\n",
    "    if i <= 1:\n",
    "        print(\"WARNING, only\",r5(CCBuse[j]),\" of a district's worth of ensemble intersected the cluster geometry\")\n",
    "        # Add more stuff here to inflate the poly and try again ...\n",
    "    else:\n",
    "        CCBpopFracThreshold[j] = HDfrac[t]\n",
    "        print(\"our final fractional capture pop to normalize this cluster's usage is\",r5(CCBpopFracThreshold[j]),\n",
    "              \"from\",nIntersections,\"intersecting HDs\" )\n",
    "    print(\"Now I will update vtd lists to move full CCB\",j,\"in or out\")\n",
    "    for t in popHDlist:\n",
    "        if HDfrac[t] > 0:\n",
    "            HDtractList[t] = list(set(HDtractList[t]).difference(CCBtL))\n",
    "            if t in addList:\n",
    "                HDtractList[t] += CCBtL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6a85cac6-b8e8-41ea-a9f1-fcf68322c2dd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, redo the unitlists and use after the CCB assignment changes\n",
      "working on converting HD 0 from vtd list to unit list\n",
      "working on converting HD 1000 from vtd list to unit list\n",
      "working on converting HD 2000 from vtd list to unit list\n",
      "working on converting HD 3000 from vtd list to unit list\n",
      "working on converting HD 4000 from vtd list to unit list\n",
      "working on converting HD 5000 from vtd list to unit list\n",
      "working on converting HD 6000 from vtd list to unit list\n",
      "working on converting HD 7000 from vtd list to unit list\n",
      "orig unit avg and sd usage are 1.00152 0.16838\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now re-calculate HD pops based on unit assignments\n"
     ]
    },
    {
     "data": {
      "image/png": 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xRgsXLpTf71eXLl00cuRIffDBB2E1oVBIs2bNUmJiorp27aq8vDwdPXo0rKa2tlb5+flyHEeO4yg/P191dXVtPkAAANA5tTnEfPHFFxowYICWLFnS4vZf/vKXeuaZZ7RkyRK988478vl8+v73v6+TJ0+6NYWFhdqwYYPWrl2r7du3q76+Xrm5uTp9+rRbM3XqVFVUVKi4uFjFxcWqqKhQfn7+RRwiAADojDzGGHPRO3s82rBhg+644w5JZ87C+P1+FRYW6tFHH5V05qxLcnKyfvGLX+j+++9XIBDQtddeq1WrVmnSpEmSpGPHjik1NVWbN29Wdna29u3bp5tuukk7d+5UZmamJGnnzp0aNmyYPvroI/Xt27fVuQWDQTmOo0AgoPj4+Is9RFxBes3fFJFxDi4aF5FxAOBK1Jb374heE1NZWanq6mplZWW567xer0aMGKGysjJJ0u7du9XY2BhW4/f7lZ6e7tbs2LFDjuO4AUaShg4dKsdx3BoAAHBli47kYNXV1ZKk5OTksPXJyck6dOiQWxMbG6tu3bo1q/l6/+rqaiUlJTUbPykpya05WygUUigUch8Hg8GLPxAAANDhtcvdSR6PJ+yxMabZurOdXdNS/fnGKSoqci8CdhxHqampFzFzAABgi4iGGJ/PJ0nNzpbU1NS4Z2d8Pp8aGhpUW1t73prjx483G//zzz9vdpbnawsWLFAgEHCXI0eO/N3HAwAAOq6IhpjevXvL5/OppKTEXdfQ0KCtW7dq+PDhkqTBgwcrJiYmrKaqqkp79+51a4YNG6ZAIKBdu3a5NW+//bYCgYBbczav16v4+PiwBQAAdF5tviamvr5en3zyifu4srJSFRUVSkhIUM+ePVVYWKinnnpKffr0UZ8+ffTUU0/pmmuu0dSpUyVJjuNoxowZmjt3rrp3766EhATNmzdPGRkZGjNmjCSpX79+Gjt2rAoKCrRs2TJJ0n333afc3NwLujMJAAB0fm0OMe+++65GjRrlPp4zZ44kadq0aVq5cqUeeeQRnTp1Sg888IBqa2uVmZmp1157TXFxce4+ixcvVnR0tCZOnKhTp05p9OjRWrlypaKiotyaNWvWaPbs2e5dTHl5eef8bhoAAHDl+bu+J6Yj43ti0FZ8TwwAXH6X7XtiAAAALhVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKwUfbknAHQ2veZvarXm4KJxl2AmANC5cSYGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArBTxENOrVy95PJ5my4MPPihJmj59erNtQ4cODRsjFApp1qxZSkxMVNeuXZWXl6ejR49GeqoAAMBiEQ8x77zzjqqqqtylpKREkvSDH/zArRk7dmxYzebNm8PGKCws1IYNG7R27Vpt375d9fX1ys3N1enTpyM9XQAAYKnoSA947bXXhj1etGiRbrjhBo0YMcJd5/V65fP5Wtw/EAho+fLlWrVqlcaMGSNJWr16tVJTU1VaWqrs7OxITxkAAFioXa+JaWho0OrVq3XPPffI4/G46998800lJSXpxhtvVEFBgWpqatxtu3fvVmNjo7Kystx1fr9f6enpKisrO+dzhUIhBYPBsAUAAHRe7RpiNm7cqLq6Ok2fPt1dl5OTozVr1uiNN97Q008/rXfeeUe33XabQqGQJKm6ulqxsbHq1q1b2FjJycmqrq4+53MVFRXJcRx3SU1NbZdjAgAAHUPEP076puXLlysnJ0d+v99dN2nSJPfv9PR0DRkyRGlpadq0aZMmTJhwzrGMMWFnc862YMECzZkzx30cDAYJMgAAdGLtFmIOHTqk0tJSrV+//rx1KSkpSktL04EDByRJPp9PDQ0Nqq2tDTsbU1NTo+HDh59zHK/XK6/XG5nJAwCADq/dPk5asWKFkpKSNG7cuPPWnThxQkeOHFFKSookafDgwYqJiXHvapKkqqoq7d2797whBgAAXFna5UxMU1OTVqxYoWnTpik6+m9PUV9fr4ULF+quu+5SSkqKDh48qMcee0yJiYm68847JUmO42jGjBmaO3euunfvroSEBM2bN08ZGRnu3UoAAADtEmJKS0t1+PBh3XPPPWHro6KitGfPHr3wwguqq6tTSkqKRo0apXXr1ikuLs6tW7x4saKjozVx4kSdOnVKo0eP1sqVKxUVFdUe0wUAABbyGGPM5Z5EewgGg3IcR4FAQPHx8Zd7OrBAr/mbLtlzHVx0/o9ZAeBK1Zb3b347CQAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGCliIeYhQsXyuPxhC0+n8/dbozRwoUL5ff71aVLF40cOVIffPBB2BihUEizZs1SYmKiunbtqry8PB09ejTSUwUAABZrlzMx/fv3V1VVlbvs2bPH3fbLX/5SzzzzjJYsWaJ33nlHPp9P3//+93Xy5Em3prCwUBs2bNDatWu1fft21dfXKzc3V6dPn26P6QIAAAtFt8ug0dFhZ1++ZozRs88+q5/85CeaMGGCJOmPf/yjkpOT9eKLL+r+++9XIBDQ8uXLtWrVKo0ZM0aStHr1aqWmpqq0tFTZ2dntMWUAAGCZdjkTc+DAAfn9fvXu3VuTJ0/WX/7yF0lSZWWlqqurlZWV5dZ6vV6NGDFCZWVlkqTdu3ersbExrMbv9ys9Pd2taUkoFFIwGAxbAABA5xXxEJOZmakXXnhBr776qp5//nlVV1dr+PDhOnHihKqrqyVJycnJYfskJye726qrqxUbG6tu3bqds6YlRUVFchzHXVJTUyN8ZAAAoCOJeIjJycnRXXfdpYyMDI0ZM0abNm2SdOZjo695PJ6wfYwxzdadrbWaBQsWKBAIuMuRI0f+jqMAAAAdXbtcE/NNXbt2VUZGhg4cOKA77rhD0pmzLSkpKW5NTU2Ne3bG5/OpoaFBtbW1YWdjampqNHz48HM+j9frldfrbZ+DACKs1/xNrdYcXDTuEswEAOzV7t8TEwqFtG/fPqWkpKh3797y+XwqKSlxtzc0NGjr1q1uQBk8eLBiYmLCaqqqqrR3797zhhgAAHBlifiZmHnz5mn8+PHq2bOnampq9POf/1zBYFDTpk2Tx+NRYWGhnnrqKfXp00d9+vTRU089pWuuuUZTp06VJDmOoxkzZmju3Lnq3r27EhISNG/ePPfjKQAAAKkdQszRo0c1ZcoU/c///I+uvfZaDR06VDt37lRaWpok6ZFHHtGpU6f0wAMPqLa2VpmZmXrttdcUFxfnjrF48WJFR0dr4sSJOnXqlEaPHq2VK1cqKioq0tMFAACW8hhjzOWeRHsIBoNyHEeBQEDx8fGXezqwwIVcp3IpcU0MgCtRW96/+e0kAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACtFPMQUFRXpO9/5juLi4pSUlKQ77rhD+/fvD6uZPn26PB5P2DJ06NCwmlAopFmzZikxMVFdu3ZVXl6ejh49GunpAgAAS0U8xGzdulUPPvigdu7cqZKSEn311VfKysrSF198EVY3duxYVVVVucvmzZvDthcWFmrDhg1au3attm/frvr6euXm5ur06dORnjIAALBQdKQHLC4uDnu8YsUKJSUlaffu3fre977nrvd6vfL5fC2OEQgEtHz5cq1atUpjxoyRJK1evVqpqakqLS1VdnZ2pKcNAAAs0+7XxAQCAUlSQkJC2Po333xTSUlJuvHGG1VQUKCamhp32+7du9XY2KisrCx3nd/vV3p6usrKytp7ygAAwAIRPxPzTcYYzZkzR7feeqvS09Pd9Tk5OfrBD36gtLQ0VVZW6mc/+5luu+027d69W16vV9XV1YqNjVW3bt3CxktOTlZ1dXWLzxUKhRQKhdzHwWCwfQ4KVuo1f9PlngIAIMLaNcTMnDlT77//vrZv3x62ftKkSe7f6enpGjJkiNLS0rRp0yZNmDDhnOMZY+TxeFrcVlRUpCeeeCIyEwcAAB1eu32cNGvWLL388svasmWLevTocd7alJQUpaWl6cCBA5Ikn8+nhoYG1dbWhtXV1NQoOTm5xTEWLFigQCDgLkeOHInMgQAAgA4p4iHGGKOZM2dq/fr1euONN9S7d+9W9zlx4oSOHDmilJQUSdLgwYMVExOjkpISt6aqqkp79+7V8OHDWxzD6/UqPj4+bAEAAJ1XxD9OevDBB/Xiiy/qz3/+s+Li4txrWBzHUZcuXVRfX6+FCxfqrrvuUkpKig4ePKjHHntMiYmJuvPOO93aGTNmaO7cuerevbsSEhI0b948ZWRkuHcrAQCAK1vEQ8xzzz0nSRo5cmTY+hUrVmj69OmKiorSnj179MILL6iurk4pKSkaNWqU1q1bp7i4OLd+8eLFio6O1sSJE3Xq1CmNHj1aK1euVFRUVKSnDAAALOQxxpjLPYn2EAwG5TiOAoEAHy3ByruTDi4ad7mnAACXXFvev/ntJAAAYCVCDAAAsBIhBgAAWIkQAwAArNSu39gL4OJdyMXIXPwL4ErGmRgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEp82R2sZ+MvVAMA/n6ciQEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlfjtJMBiF/K7UQcXjbsEMwGAS48zMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAVuIWa6CTu5DbsC8Et2oD6GgIMejQIvUGDADofPg4CQAAWIkQAwAArMTHSQAuCD9xAKCj4UwMAACwEmdi0GZcbAsA6Ag6fIhZunSpfvWrX6mqqkr9+/fXs88+q+9+97uXe1oAWsBHTgAupQ4dYtatW6fCwkItXbpUt9xyi5YtW6acnBx9+OGH6tmz5+WeHoB2QhgCcCE8xhhzuSdxLpmZmRo0aJCee+45d12/fv10xx13qKio6Lz7BoNBOY6jQCCg+Pj49p7qFYWPk9ARRCrERCowXcnBy9Zj54sgO6a2vH932BDT0NCga665Ri+99JLuvPNOd/1DDz2kiooKbd26Naw+FAopFAq5jwOBgHr27KkjR460S4hJf/zVVmv2PpF9yca5EBfyXEBnEqn/Bi+lS/n/jc567LbqaO8Zl0swGFRqaqrq6urkOM75i00H9dlnnxlJ5r//+7/D1v/Hf/yHufHGG5vVP/7440YSCwsLCwsLSydYjhw50mpW6NDXxEiSx+MJe2yMabZOkhYsWKA5c+a4j5uamvS///u/6t69e4v17eHr9NheZ3+uRPQ08uhpZNHPyKOnkWdTT40xOnnypPx+f6u1HTbEJCYmKioqStXV1WHra2pqlJyc3Kze6/XK6/WGrfvWt77VnlM8p/j4+A7/IrENPY08ehpZ9DPy6Gnk2dLTVj9G+n8d9svuYmNjNXjwYJWUlIStLykp0fDhwy/TrAAAQEfRYc/ESNKcOXOUn5+vIUOGaNiwYfr973+vw4cP60c/+tHlnhoAALjMOnSImTRpkk6cOKEnn3xSVVVVSk9P1+bNm5WWlna5p9Yir9erxx9/vNnHWrh49DTy6Glk0c/Io6eR11l72mFvsQYAADifDntNDAAAwPkQYgAAgJUIMQAAwEqEGAAAYKUrKsT06tVLHo+n2fLggw82q73//vvl8Xj07LPPhq0PhUKaNWuWEhMT1bVrV+Xl5eno0aNhNbW1tcrPz5fjOHIcR/n5+aqrqwurOXz4sMaPH6+uXbsqMTFRs2fPVkNDQ1jNnj17NGLECHXp0kXXXXednnzySXWk67AvpJ/79u1TXl6eHMdRXFychg4dqsOHD7vb6We41npaX1+vmTNnqkePHurSpYv69esX9gOpEj0921dffaWf/vSn6t27t7p06aLrr79eTz75pJqamtwaY4wWLlwov9+vLl26aOTIkfrggw/CxqGvZ7TWz8bGRj366KPKyMhQ165d5ff79cMf/lDHjh0LG4d+/s2FvEa/ifenb/j7fuHILjU1NaaqqspdSkpKjCSzZcuWsLoNGzaYAQMGGL/fbxYvXhy27Uc/+pG57rrrTElJiSkvLzejRo0yAwYMMF999ZVbM3bsWJOenm7KyspMWVmZSU9PN7m5ue72r776yqSnp5tRo0aZ8vJyU1JSYvx+v5k5c6ZbEwgETHJyspk8ebLZs2eP+dOf/mTi4uLMr3/963bpzcVorZ+ffPKJSUhIMA8//LApLy83n376qXnllVfM8ePH3THoZ7jWenrvvfeaG264wWzZssVUVlaaZcuWmaioKLNx40Z3DHoa7uc//7np3r27eeWVV0xlZaV56aWXzD/8wz+YZ5991q1ZtGiRiYuLM3/605/Mnj17zKRJk0xKSooJBoNuDX09o7V+1tXVmTFjxph169aZjz76yOzYscNkZmaawYMHh41DP//mQl6jX+P9KdwVFWLO9tBDD5kbbrjBNDU1ueuOHj1qrrvuOrN3716TlpYW9iKpq6szMTExZu3ate66zz77zFx11VWmuLjYGGPMhx9+aCSZnTt3ujU7duwwksxHH31kjDFm8+bN5qqrrjKfffaZW/Nf//Vfxuv1mkAgYIwxZunSpcZxHPPll1+6NUVFRcbv94fNtyM5u5+TJk0y//Zv/3bOevrZurN72r9/f/Pkk0+G1QwaNMj89Kc/NcbQ05aMGzfO3HPPPWHrJkyY4L42m5qajM/nM4sWLXK3f/nll8ZxHPO73/3OGENfv6m1frZk165dRpI5dOiQMYZ+nu1Ce8r7U3NX1MdJ39TQ0KDVq1frnnvucX8gsqmpSfn5+Xr44YfVv3//Zvvs3r1bjY2NysrKctf5/X6lp6errKxMkrRjxw45jqPMzEy3ZujQoXIcJ6wmPT097MetsrOzFQqFtHv3brdmxIgRYV9MlJ2drWPHjungwYORa0SEnN3PpqYmbdq0STfeeKOys7OVlJSkzMxMbdy40d2Hfp5fS6/RW2+9VS+//LI+++wzGWO0ZcsWffzxx8rOzpZET1ty66236vXXX9fHH38sSXrvvfe0fft23X777ZKkyspKVVdXh/XM6/VqxIgRbj/o69+01s+WBAIBeTwe9/fs6Ge4C+kp708tu2JDzMaNG1VXV6fp06e7637xi18oOjpas2fPbnGf6upqxcbGqlu3bmHrk5OT3R+qrK6uVlJSUrN9k5KSwmrO/hHLbt26KTY29rw1Xz8++0cxO4Kz+1lTU6P6+notWrRIY8eO1WuvvaY777xTEyZM0NatWyXRz9a09Br9z//8T910003q0aOHYmNjNXbsWC1dulS33nqrJHrakkcffVRTpkzRP/3TPykmJkYDBw5UYWGhpkyZIulvc23pWL55rPT1jNb6ebYvv/xS8+fP19SpU90fHqSf4S6kp7w/taxD/+xAe1q+fLlycnLctLl792795je/UXl5ufuv3gtljAnbp6X9I1Fj/v+iqbbO71I4u59fX5D2L//yL/rxj38sSfr2t7+tsrIy/e53v9OIESPOORb9POPsnkpnQszOnTv18ssvKy0tTdu2bdMDDzyglJQUjRkz5pxjXck9XbdunVavXq0XX3xR/fv3V0VFhQoLC+X3+zVt2jS3rqVjae04rsS+Xmg/pTMX+U6ePFlNTU1aunRpq2Nfif2UWu8p70/ndkWeiTl06JBKS0t17733uuveeust1dTUqGfPnoqOjlZ0dLQOHTqkuXPnqlevXpIkn8+nhoYG1dbWho1XU1PjplCfz6fjx483e87PP/88rObstFpbW6vGxsbz1tTU1Ehq/i/Gy62lfiYmJio6Olo33XRTWG2/fv3cu5Po57m11NNTp07pscce0zPPPKPx48fr5ptv1syZMzVp0iT9+te/lkRPW/Lwww9r/vz5mjx5sjIyMpSfn68f//jHKioqknTmOKTm/4I8u2f09YzW+vm1xsZGTZw4UZWVlSopKXHPwkj082yt9ZT3p3O7IkPMihUrlJSUpHHjxrnr8vPz9f7776uiosJd/H6/Hn74Yb366quSpMGDBysmJkYlJSXuflVVVdq7d6+GDx8uSRo2bJgCgYB27drl1rz99tsKBAJhNXv37lVVVZVb89prr8nr9Wrw4MFuzbZt28Jua3vttdfk9/vdF21H0VI/Y2Nj9Z3vfEf79+8Pq/3444/dH/Ckn+fWUk8bGxvV2Nioq64K/882KirKPfNFT5v761//et6e9e7dWz6fL6xnDQ0N2rp1q9sP+vo3rfVT+luAOXDggEpLS9W9e/ewevoZrrWe8v50HpfqCuKO4vTp06Znz57m0UcfbbX27Ku/jTlzC1uPHj1MaWmpKS8vN7fddluLt7DdfPPNZseOHWbHjh0mIyOjxVvYRo8ebcrLy01paanp0aNH2C1sdXV1Jjk52UyZMsXs2bPHrF+/3sTHx3eo2wKNOX8/169fb2JiYszvf/97c+DAAfPb3/7WREVFmbfeesutoZ/Nna+nI0aMMP379zdbtmwxf/nLX8yKFSvM1VdfbZYuXerW0NNw06ZNM9ddd517++r69etNYmKieeSRR9yaRYsWGcdxzPr1682ePXvMlClTWrzFmr623s/GxkaTl5dnevToYSoqKsK+MiAUCrnj0M+/uZDX6Nl4fzrjigsxr776qpFk9u/f32ptSy+SU6dOmZkzZ5qEhATTpUsXk5ubaw4fPhxWc+LECXP33XebuLg4ExcXZ+6++25TW1sbVnPo0CEzbtw406VLF5OQkGBmzpwZdruaMca8//775rvf/a7xer3G5/OZhQsXdphbAr/WWj+XL19u/vEf/9FcffXVZsCAAWHfZ2IM/WzJ+XpaVVVlpk+fbvx+v7n66qtN3759zdNPPx12HPQ0XDAYNA899JDp2bOnufrqq831119vfvKTn4S9oTY1NZnHH3/c+Hw+4/V6zfe+9z2zZ8+esHHo6xmt9bOystJIanH55ndy0c+/uZDX6Nl4fzrDY0wH+tpCAACAC3RFXhMDAADsR4gBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJX+DzUNr7rOBwDAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#skip for FL; already squared up\n",
    "print(\"OK, redo the unitlists and use after the CCB assignment changes\")\n",
    "HDunitList = [list() for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "    for v in HDtractList[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "        if v in allUnits:\n",
    "            HDunitList[t].append(allUnits.index(v))\n",
    "    for c in unitCounties:\n",
    "        if countyTractList[c][0] in HDtractList[t]:\n",
    "            u = allUnits.index(c+0.5)\n",
    "            HDunitList[t].append(u)\n",
    "    for j, L in enumerate(CCBlist):\n",
    "        c = L[0]\n",
    "        if countyTractList[c][0] in HDtractList[t]:\n",
    "            u = allUnits.index(j+0.25)\n",
    "            HDunitList[t].append(u) \n",
    "            unitUse = [0. for u in range(nUnits)]\n",
    "            \n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD\n",
    "\n",
    "print(\"Now re-calculate HD pops based on unit assignments\")\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b99795f-d030-456d-9201-6519b739b472",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dc26d08c-a5d9-4e7e-9da5-ae98cf658a3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If we just ran option 2 with no tracts bigger than vtds, run this but with initial ten rows commented out\n",
      "this optional RESTART block pulls in an existing vtdlist for patching\n",
      "Must have already established unitGeoms, pops, topology\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched vtdlist, e.g. ./2024state_HD_output/CO3108contigUnpatchedB.csv ./2024state_HD_output/FL7211needsEnclaveRemoval.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in 7211 HD lists of vtds\n",
      "working on converting HD 0 from vtd list to unit list\n",
      "working on converting HD 500 from vtd list to unit list\n",
      "working on converting HD 1000 from vtd list to unit list\n",
      "working on converting HD 1500 from vtd list to unit list\n",
      "working on converting HD 2000 from vtd list to unit list\n",
      "working on converting HD 2500 from vtd list to unit list\n",
      "working on converting HD 3000 from vtd list to unit list\n",
      "working on converting HD 3500 from vtd list to unit list\n",
      "working on converting HD 4000 from vtd list to unit list\n",
      "working on converting HD 4500 from vtd list to unit list\n",
      "working on converting HD 5000 from vtd list to unit list\n",
      "working on converting HD 5500 from vtd list to unit list\n",
      "working on converting HD 6000 from vtd list to unit list\n",
      "working on converting HD 6500 from vtd list to unit list\n",
      "working on converting HD 7000 from vtd list to unit list\n",
      "orig unit avg and sd usage are 1.00435 0.16718\n"
     ]
    },
    {
     "data": {
      "image/png": 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Le329pKTEYzovL095eXleFwUAAEaOAZ8zAgAAMBCEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGBZouAEOn/sx5nXa6enz9eNM5H1YDAED3CCN+qv7Med36ZIXOt3f0Os4eFKAxYcE+qgoAgK4II37qtNOl8+0dKrrrek2LCe9x3JiwYE2IsvuwMgAAPHl1zsiGDRs0c+ZMRUREKCIiQmlpaXrzzTd7naeiokIpKSkKDQ3V1KlTtXHjxgEVDO9MiwlX8oTIHhtBBABgmldhZOLEiXr88cdVVVWlqqoq3XLLLbrjjjv08ccfdzv+5MmTysrKUnp6uqqrq7Vu3TqtWrVKpaWlg1I8AAAY/rw6TLN48WKP6ccee0wbNmzQgQMHlJSU1GX8xo0bNWnSJBUVFUmSrr32WlVVVamwsFBLlizpf9UAAMBv9PvS3o6ODm3btk1Op1NpaWndjtm/f78yMzM9+hYuXKiqqiq1t7f3+N5tbW1qbW31aAAAwD95HUYOHTqk8PBwhYSEKDs7Wzt37tSMGTO6HdvY2KjY2FiPvtjYWF24cEHNzc09LqOgoECRkZHuFh8f722ZAABgmPA6jCQmJqqmpkYHDhzQfffdp2XLlunw4cM9jrfZbB7TlmV12/+H8vPz1dLS4m51dXXelgkAAIYJry/tDQ4O1rRp0yRJqampev/99/X000/r+eef7zJ2/Pjxamxs9OhrampSYGCgoqOje1xGSEiIQkJCvC0NAAAMQwO+HbxlWWpra+v2tbS0NJWVlXn07dmzR6mpqQoKChroogEAgB/wKoysW7dO77zzjj7//HMdOnRIDz/8sMrLy/U3f/M3kr49vHL33Xe7x2dnZ+vUqVPKzc3VkSNHtHnzZhUXF2vNmjWD+ykAAMCw5dVhmt/97ndaunSpGhoaFBkZqZkzZ2r37t267bbbJEkNDQ2qra11j09ISNCuXbu0evVqPfvss4qLi9P69eu5rBcAALh5FUaKi4t7fb2kpKRL3/z58/XBBx94VRQAABg5BnzOCAAAwEAQRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgVKDpAuA/jjed6/X1MWHBmhBl91E1AIDhgjCCARsTFix7UIByttf0Os4eFKC3HpxPIAEAeCCMYMAmRNn11oPzddrp6nHM8aZzytleo9NOF2EEAOCBMIJBMSHKTsgAAPQLJ7ACAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACM8iqMFBQU6MYbb9To0aMVExOjO++8U0ePHu11nvLyctlsti7tk08+GVDhAADAP3gVRioqKrR8+XIdOHBAZWVlunDhgjIzM+V0Oi8579GjR9XQ0OBu06dP73fRAADAfwR6M3j37t0e0y+++KJiYmJ08OBBzZs3r9d5Y2JiFBUV5XWBAADAvw3onJGWlhZJ0tixYy85dvbs2XI4HMrIyNDevXt7HdvW1qbW1laPBgAA/FO/w4hlWcrNzdXcuXOVnJzc4ziHw6FNmzaptLRUO3bsUGJiojIyMlRZWdnjPAUFBYqMjHS3+Pj4/pYJAAAuc14dpvlDK1as0Icffqh3332313GJiYlKTEx0T6elpamurk6FhYU9HtrJz89Xbm6ue7q1tZVAAgCAn+rXnpGVK1fq1Vdf1d69ezVx4kSv558zZ46OHTvW4+shISGKiIjwaAAAwD95tWfEsiytXLlSO3fuVHl5uRISEvq10Orqajkcjn7NCwAA/ItXYWT58uX69a9/rf/8z//U6NGj1djYKEmKjIyU3W6X9O0hlvr6em3ZskWSVFRUpClTpigpKUkul0tbt25VaWmpSktLB/mjAACA4cirMLJhwwZJ0oIFCzz6X3zxRf3d3/2dJKmhoUG1tbXu11wul9asWaP6+nrZ7XYlJSXpjTfeUFZW1sAqBwAAfsHrwzSXUlJS4jGdl5envLw8r4oCAAAjB8+mAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYJRXYaSgoEA33nijRo8erZiYGN155506evToJeerqKhQSkqKQkNDNXXqVG3cuLHfBQMAAP/iVRipqKjQ8uXLdeDAAZWVlenChQvKzMyU0+nscZ6TJ08qKytL6enpqq6u1rp167Rq1SqVlpYOuHgAADD8BXozePfu3R7TL774omJiYnTw4EHNmzev23k2btyoSZMmqaioSJJ07bXXqqqqSoWFhVqyZEn/qgYAAH5jQOeMtLS0SJLGjh3b45j9+/crMzPTo2/hwoWqqqpSe3t7t/O0tbWptbXVowEAAP/U7zBiWZZyc3M1d+5cJScn9ziusbFRsbGxHn2xsbG6cOGCmpubu52noKBAkZGR7hYfH9/fMgEAwGWu32FkxYoV+vDDD/Xyyy9fcqzNZvOYtiyr2/6L8vPz1dLS4m51dXX9LRMAAFzmvDpn5KKVK1fq1VdfVWVlpSZOnNjr2PHjx6uxsdGjr6mpSYGBgYqOju52npCQEIWEhPSnNAAAMMx4tWfEsiytWLFCO3bs0Ntvv62EhIRLzpOWlqaysjKPvj179ig1NVVBQUHeVQsAAPyOV2Fk+fLl2rp1q379619r9OjRamxsVGNjo86fP+8ek5+fr7vvvts9nZ2drVOnTik3N1dHjhzR5s2bVVxcrDVr1gzepwAAAMOWV2Fkw4YNamlp0YIFC+RwONxt+/bt7jENDQ2qra11TyckJGjXrl0qLy/X9ddfr3/+53/W+vXruawXAABI8vKckYsnnvampKSkS9/8+fP1wQcfeLMoAAAwQvBsGgAAYBRhBAAAGNWvS3uB/jredK7X18eEBWtClN1H1QAALgeEEfjEmLBg2YMClLO9ptdx9qAAvfXgfAIJAIwghBH4xIQou956cL5OO109jjnedE4522t02ukijADACEIYgc9MiLITMgAAXXACKwAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwKhA0wXAe/Vnzuu009XrmONN53xUDQAAA0MYGWbqz5zXrU9W6Hx7xyXH2oMCNCYs2AdVAQDQf4SRYea006Xz7R0quut6TYsJ73XsmLBgTYiy+6gyAAD6hzAyTE2LCVfyhEjTZQAAMGCcwAoAAIzyOoxUVlZq8eLFiouLk81m0yuvvNLr+PLyctlsti7tk08+6W/NAADAj3h9mMbpdGrWrFm65557tGTJkj7Pd/ToUUVERLinx40b5+2iAQCAH/I6jCxatEiLFi3yekExMTGKioryej4AAODffHbOyOzZs+VwOJSRkaG9e/f2OratrU2tra0eDQAA+KchDyMOh0ObNm1SaWmpduzYocTERGVkZKiysrLHeQoKChQZGelu8fHxQ10mAAAwZMgv7U1MTFRiYqJ7Oi0tTXV1dSosLNS8efO6nSc/P1+5ubnu6dbWVgIJAAB+ysilvXPmzNGxY8d6fD0kJEQREREeDQAA+CcjYaS6uloOh8PEogEAwGXG68M0586d0/Hjx93TJ0+eVE1NjcaOHatJkyYpPz9f9fX12rJliySpqKhIU6ZMUVJSklwul7Zu3arS0lKVlpYO3qcAAADDltdhpKqqSjfffLN7+uK5HcuWLVNJSYkaGhpUW1vrft3lcmnNmjWqr6+X3W5XUlKS3njjDWVlZQ1C+QAAYLjzOowsWLBAlmX1+HpJSYnHdF5envLy8rwuDAAAjAw8mwYAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGBVougDgjx1vOtfr62PCgjUhyu6jagAAQ40wgsvGmLBg2YMClLO9ptdx9qAAvfXgfAIJAPgJwgguGxOi7Hrrwfk67XT1OOZ40znlbK/RaaeLMAIAfoIwgsvKhCg7IQMARhhOYAUAAEYRRgAAgFFeh5HKykotXrxYcXFxstlseuWVVy45T0VFhVJSUhQaGqqpU6dq48aN/akVAAD4Ia/DiNPp1KxZs/TMM8/0afzJkyeVlZWl9PR0VVdXa926dVq1apVKS0u9LhYAAPgfr09gXbRokRYtWtTn8Rs3btSkSZNUVFQkSbr22mtVVVWlwsJCLVmyxNvFAwAAPzPk54zs379fmZmZHn0LFy5UVVWV2tvbu52nra1Nra2tHg0AAPinIQ8jjY2Nio2N9eiLjY3VhQsX1Nzc3O08BQUFioyMdLf4+PihLhMAABjik6tpbDabx7RlWd32X5Sfn6+WlhZ3q6urG/IaAQCAGUN+07Px48ersbHRo6+pqUmBgYGKjo7udp6QkBCFhIQMdWkAAOAyMOR7RtLS0lRWVubRt2fPHqWmpiooKGioFw8AAC5zXoeRc+fOqaamRjU1NZK+vXS3pqZGtbW1kr49xHL33Xe7x2dnZ+vUqVPKzc3VkSNHtHnzZhUXF2vNmjWD8wkAAMCw5vVhmqqqKt18883u6dzcXEnSsmXLVFJSooaGBncwkaSEhATt2rVLq1ev1rPPPqu4uDitX7+ey3oBAICkfoSRBQsWuE9A7U5JSUmXvvnz5+uDDz7wdlEAAGAE4Nk0AADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowJNFwBP9WfO67TT1ePrx5vO+bAaAACGHmHkMlJ/5rxufbJC59s7eh1nDwrQmLBgH1UFAMDQIoxcRk47XTrf3qGiu67XtJjwHseNCQvWhCi7DysDAGDoEEYuQ9NiwpU8IdJ0GQAA+AQnsAIAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAqH6Fkeeee04JCQkKDQ1VSkqK3nnnnR7HlpeXy2azdWmffPJJv4sGAAD+w+swsn37duXk5Ojhhx9WdXW10tPTtWjRItXW1vY639GjR9XQ0OBu06dP73fRAADAf3h907OnnnpKP/jBD/TDH/5QklRUVKT/+q//0oYNG1RQUNDjfDExMYqKiup3ocAfutQzerhLLQAMH16FEZfLpYMHD2rt2rUe/ZmZmXrvvfd6nXf27Nn65ptvNGPGDD3yyCO6+eabexzb1tamtrY293Rra6s3ZcKPjQkLlj0oQDnba3odZw8K0FsPzieQAMAw4FUYaW5uVkdHh2JjYz36Y2Nj1djY2O08DodDmzZtUkpKitra2vTSSy8pIyND5eXlmjdvXrfzFBQU6NFHH/WmNIwQE6LseuvB+Zd8snHO9hqddroIIwAwDPTr2TQ2m81j2rKsLn0XJSYmKjEx0T2dlpamuro6FRYW9hhG8vPzlZub655ubW1VfHx8f0qFH5oQZSdkAIAf8eoE1iuvvFIBAQFd9oI0NTV12VvSmzlz5ujYsWM9vh4SEqKIiAiPBgAA/JNXYSQ4OFgpKSkqKyvz6C8rK9NNN93U5/eprq6Ww+HwZtEAAMBPeX2YJjc3V0uXLlVqaqrS0tK0adMm1dbWKjs7W9K3h1jq6+u1ZcsWSd9ebTNlyhQlJSXJ5XJp69atKi0tVWlp6eB+EgAAMCx5HUbuuusuffnll/rpT3+qhoYGJScna9euXZo8ebIkqaGhweOeIy6XS2vWrFF9fb3sdruSkpL0xhtvKCsra/A+BQAAGLb6dQLr/fffr/vvv7/b10pKSjym8/LylJeX15/FAACAEYBn0wAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMCrQdAHAUDnedK7X18eEBWtClN1H1QAAekIYgd8ZExYse1CAcrbX9DrOHhSgtx6cTyABAMMII/A7E6LseuvB+TrtdPU45njTOeVsr9Fpp4swAgCGEUbglyZE2QkZADBMcAIrAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIzi0l4fqj9z/pL3voBvcZdWADCPMOIj9WfO69YnK3S+vaPXcfagAI0JC/ZRVSMXd2kFgMsHYcRHTjtdOt/eoaK7rte0mPAex/E/cd/gLq0AcPkgjPjYtJhwJU+INF0GxF1aAeByQRgBLoHzSgBgaBFGgB5wXgkA+AZhBOgB55UAgG8QRoBecF4JAAw9bnoGAACMYs9IH1zqZmV9wQ3NMBj6si1yQi2A4aZfYeS5557Tv/7rv6qhoUFJSUkqKipSenp6j+MrKiqUm5urjz/+WHFxccrLy1N2dna/i/alvt6srC+4oZn/8sUVN97cOG/j0hRFD3BbI9QA8BWvw8j27duVk5Oj5557Tt/5znf0/PPPa9GiRTp8+LAmTZrUZfzJkyeVlZWle++9V1u3btW+fft0//33a9y4cVqyZMmgfIiB6Mst2vtys7K+4B93/+PLK276cuO8L50uZb90UMs2/7bfy7mIq4SA/mEPpvdslmVZ3szwp3/6p7rhhhu0YcMGd9+1116rO++8UwUFBV3GP/TQQ3r11Vd15MgRd192drb+53/+R/v37+/TMltbWxUZGamWlhZFRER4U26vvPmfJv8ooyd9CbQ522sGfPfdj+pbdPu/v6vXV87t9cZ5g3VYsS8198Vg/aM7HP+BH4zvYjBdbuvHH/G74qmvv99e7RlxuVw6ePCg1q5d69GfmZmp9957r9t59u/fr8zMTI++hQsXqri4WO3t7QoKCuoyT1tbm9ra2tzTLS0tkr79UIOprrFFznNn9fj/uU5Tx4X1OC5qVLBGX9Gu1tb2QV0+/MPoK6TRo209vh7YEajgzm+0akv3f0cuCg26QkXfn62xo7r+nZCkz37vVGfb1zp3tlWtrT0v71L19EVfa+6LS32uvvjq63blbKvWN+2dQ76swdLXmn3pclo//uqz3zsv+bvy2e+dWrvjkCoOner1t8eXxoWHaFxE6KC/78Xf7Uvu97C8UF9fb0my9u3b59H/2GOPWVdffXW380yfPt167LHHPPr27dtnSbK++OKLbuf5yU9+Ykmi0Wg0Go3mB62urq7XfNGvE1htNs//dVmW1aXvUuO7678oPz9fubm57unOzk599dVXio6Ods/T2tqq+Ph41dXVDeqhG/SO9W4O694M1rs5rHszBnO9W5als2fPKi4urtdxXoWRK6+8UgEBAWpsbPTob2pqUmxsbLfzjB8/vtvxgYGBio6O7naekJAQhYSEePRFRUV1OzYiIoKN1ADWuzmsezNY7+aw7s0YrPUeGRl5yTFe3fQsODhYKSkpKisr8+gvKyvTTTfd1O08aWlpXcbv2bNHqamp3Z4vAgAARhav78Cam5urX/ziF9q8ebOOHDmi1atXq7a21n3fkPz8fN19993u8dnZ2Tp16pRyc3N15MgRbd68WcXFxVqzZs3gfQoAADBseX3OyF133aUvv/xSP/3pT9XQ0KDk5GTt2rVLkydPliQ1NDSotrbWPT4hIUG7du3S6tWr9eyzzyouLk7r168f8D1GQkJC9JOf/KTL4RwMLda7Oax7M1jv5rDuzTCx3r2+zwgAAMBg4kF5AADAKMIIAAAwijACAACMIowAAACjLusw8txzzykhIUGhoaFKSUnRO++80+PY8vJy2Wy2Lu2TTz7xYcXDX2VlpRYvXqy4uDjZbDa98sorl5ynoqJCKSkpCg0N1dSpU7Vx48ahL9TPeLve2d4HR0FBgW688UaNHj1aMTExuvPOO3X06NFLzsc2P3D9Wfds9wO3YcMGzZw5031Ds7S0NL355pu9zuOL7f2yDSPbt29XTk6OHn74YVVXVys9PV2LFi3yuGy4O0ePHlVDQ4O7TZ8+3UcV+wen06lZs2bpmWee6dP4kydPKisrS+np6aqurta6deu0atUqlZaWDnGl/sXb9X4R2/vAVFRUaPny5Tpw4IDKysp04cIFZWZmyul09jgP2/zg6M+6v4jtvv8mTpyoxx9/XFVVVaqqqtItt9yiO+64Qx9//HG34322vfftEXm+9yd/8idWdna2R98111xjrV27ttvxe/futSRZp0+f9kF1I4Mka+fOnb2OycvLs6655hqPvh/96EfWnDlzhrAy/9aX9c72PjSamposSVZFRUWPY9jmh0Zf1j3b/dAYM2aM9Ytf/KLb13y1vV+We0ZcLpcOHjyozMxMj/7MzEy9917vjzSfPXu2HA6HMjIytHfv3qEsE5L279/f5XtauHChqqqq1N7ebqiqkYPtfXC1tLRIksaOHdvjGLb5odGXdX8R2/3g6Ojo0LZt2+R0OpWWltbtGF9t75dlGGlublZHR0eXh+/FxsZ2eejeRQ6HQ5s2bVJpaal27NihxMREZWRkqLKy0hclj1iNjY3dfk8XLlxQc3Ozoar8H9v74LMsS7m5uZo7d66Sk5N7HMc2P/j6uu7Z7gfHoUOHFB4erpCQEGVnZ2vnzp2aMWNGt2N9tb17fTt4X7LZbB7TlmV16bsoMTFRiYmJ7um0tDTV1dWpsLBQ8+bNG9I6R7ruvqfu+jF42N4H34oVK/Thhx/q3XffveRYtvnB1dd1z3Y/OBITE1VTU6MzZ86otLRUy5YtU0VFRY+BxBfb+2W5Z+TKK69UQEBAl70gTU1NXRJab+bMmaNjx44Ndnn4A+PHj+/2ewoMDFR0dLShqkYmtvf+W7lypV599VXt3btXEydO7HUs2/zg8mbdd4ft3nvBwcGaNm2aUlNTVVBQoFmzZunpp5/udqyvtvfLMowEBwcrJSVFZWVlHv1lZWW66aab+vw+1dXVcjgcg10e/kBaWlqX72nPnj1KTU1VUFCQoapGJrZ371mWpRUrVmjHjh16++23lZCQcMl52OYHR3/WfXfY7gfOsiy1tbV1+5rPtvdBPR12EG3bts0KCgqyiouLrcOHD1s5OTlWWFiY9fnnn1uWZVlr1661li5d6h7/b//2b9bOnTutTz/91Proo4+stWvXWpKs0tJSUx9hWDp79qxVXV1tVVdXW5Ksp556yqqurrZOnTplWVbX9f7ZZ59Zo0aNslavXm0dPnzYKi4utoKCgqz/+I//MPURhiVv1zvb++C47777rMjISKu8vNxqaGhwt6+//to9hm1+aPRn3bPdD1x+fr5VWVlpnTx50vrwww+tdevWWVdccYW1Z88ey7LMbe+XbRixLMt69tlnrcmTJ1vBwcHWDTfc4HHJ17Jly6z58+e7p5944gnrqquuskJDQ60xY8ZYc+fOtd544w0DVQ9vFy+d++O2bNkyy7K6rnfLsqzy8nJr9uzZVnBwsDVlyhRrw4YNvi98mPN2vbO9D47u1rkk68UXX3SPYZsfGv1Z92z3A/f3f//37t/VcePGWRkZGe4gYlnmtnebZf2/M1EAAAAMuCzPGQEAACMHYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBR/xdNwI8G3D8sjgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"If we just ran option 2 with no tracts bigger than vtds, run this but with initial ten rows commented out\")\n",
    "print(\"this optional RESTART block pulls in an existing vtdlist for patching\")\n",
    "print(\"Must have already established unitGeoms, pops, topology\")\n",
    "\n",
    "#infile = input(\"enter the unpatched vtdlist, e.g. ./2024state_HD_output/CO3108contigUnpatchedB.csv\")\n",
    "#inDF = pd.read_csv(infile)\n",
    "#vtdListString = inDF[\"HDvtdList\"]\n",
    "#nHDs = len(vtdListString)\n",
    "#inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "#HDweight = inDF[\"HDweight\"]\n",
    "#HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "#hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "#hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "#print(\"I read in\",nHDs,\"HD lists of vtds\")\n",
    "\n",
    "HDunitList = [list() for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if t%500 == 0:\n",
    "        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "        if v in allUnits:\n",
    "            HDunitList[t].append(allUnits.index(v))\n",
    "    for c in unitCounties:\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(c+0.5)\n",
    "            HDunitList[t].append(u)\n",
    "    for j, L in enumerate(CCBlist):\n",
    "        c = L[0]\n",
    "        if countyTractList[c][0] in inVTDlist[t]:\n",
    "            u = allUnits.index(j+0.25)\n",
    "            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ce9292b0-7d6a-419a-9491-d0def779ad04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 6420 populated HDs\n"
     ]
    }
   ],
   "source": [
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if len(HDunitList[t]) > 0:\n",
    "        popHDlist.append(t)\n",
    "populatedTractList = popHDlist.copy()\n",
    "print(\"there are a total of\",len(populatedTractList),\"populated HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f7f36559-7f1d-4203-8b24-0e8cc2ce1753",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this will show if we had any duplicated units in HDunitLists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this will show if we had any duplicated units in HDunitLists\")\n",
    "excess = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    excess[t] = len(HDunitList[t]) - len(set(HDunitList[t]))\n",
    "plt.hist(excess)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "27bc7f0a-d7ff-4c68-830f-29ba5013c314",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#popHDlist = populatedTractList.copy()  #somehow our popHDlist started to include the cut District HD starters\n",
    "HDunitList = [list(set(HDunitList[t])) for t in range(nHDs)]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9175ad93-3ead-4f5a-8e58-1ae75653f8bd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "714 219.78177952766418\n"
     ]
    }
   ],
   "source": [
    "print(t,time.time() - startTime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "af8a4945-40c2-40df-8dd4-d516f4de2f0f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 8 time is now 0\n",
      "working on HD 1560 time is now 342\n",
      "working on HD 2899 time is now 484\n",
      "working on HD 3968 time is now 716\n",
      "working on HD 5004 time is now 868\n",
      "working on HD 6071 time is now 1125\n",
      "out of 5979 total HDs, there were 4273.0 contiguous and 4499.0 complement-contiguous HDs\n",
      "501 HDs had both discontiguity problems, while enclave-only = 979 and discontig only= 1205\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 1706 979\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"FIND DISCO\" CODE - continue here for option 1 or option 2 drawing method\n",
    "# 3/4/24 - for FL, using 6 or unspecified loops now yields same results\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%1000 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,6) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "477e7130-929c-4c92-9fce-2dd14a9b0256",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 7 HDs with enclave pops > aDP\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bigList = list()\n",
    "ePop = [sum(unitPop[u] for u in e) for e in enclaveLists]\n",
    "for i, eL in enumerate(enclaveLists):\n",
    "    t = eLgenerator[i]\n",
    "    if ePop[i] > aDP:\n",
    "        bigList.append(t)\n",
    "print(\"there are\",len(bigList),\"HDs with enclave pops > aDP\")  #likely NE corner\n",
    "for t in bigList:\n",
    "    plotPoly(hdCP[t].buffer(0.1))\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53ab9f20-ddb8-47b1-9e5d-5cfda17b2b25",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "40f06e44-45be-4a36-b62c-26698af70e06",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 730896 district pop vs 769364 target\n",
      "we'll also trim any HD with total islands' pop <= 15387\n",
      "working on HD 84\n",
      "working on HD 656\n",
      "working on HD 1170\n",
      "working on HD 1842\n",
      "working on HD 2119\n",
      "working on HD 2495\n",
      "working on HD 3012\n",
      "working on HD 3337\n",
      "working on HD 3698\n",
      "working on HD 3942\n",
      "working on HD 4145\n",
      "working on HD 4476\n",
      "working on HD 4727\n",
      "working on HD 5034\n",
      "working on HD 5584\n",
      "working on HD 6103\n",
      "working on HD 6623\n",
      "working on HD 7171\n",
      "Out of 1706 discontig HDs 1706 had discontig HDs.\n",
      "Of these, 1450 won't be under 730896 after all minor discontigys were shed, while 256 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "There are 256 HDs still with both discontinuity problems\n",
      "Additionally, 416 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%200 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for i, t in enumerate(sPgenerator):    \n",
    "    if i%500 == 0:\n",
    "        print(\"reclassifying HD\",t)\n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "4037f8cb-661b-4032-bfb9-e147975e3bbb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/13/24 - classify further: ID those with adjoiners intersecting the map boundary vs internal islands\n",
      "working on enclave-generating HD 35\n",
      "working on enclave-generating HD 4153\n",
      "working on enclave-generating HD 427\n",
      "Out of 1395 HDpolys that generated enclaves, 0 had contiguous adjrs, while 1173 neighbored a state boundary while 222 had only internal enclaves\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to print the total enclave pops for all enclavy HDs.  This takes a few min; not required 0\n"
     ]
    }
   ],
   "source": [
    "print(\"1/13/24 - classify further: ID those with adjoiners intersecting the map boundary vs internal islands\")\n",
    "internals, edgers, nOK = list(), list(), 0\n",
    "for i,t in enumerate(eLgenerator):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on enclave-generating HD\",t)\n",
    "    twoNbrs = get2nbrs(HDunitList[t],unitNbrs)\n",
    "    isContig, adjSublist = isContiguous(twoNbrs,unitNbrs)\n",
    "    if isContig:\n",
    "        nOK +=1\n",
    "    else:\n",
    "        if len (set(getAdjoiners(HDunitList[t],unitNbrs)).intersection(set(borderUnits)))  > 0:\n",
    "            edgers.append(t)\n",
    "        else:\n",
    "            internals.append(t)\n",
    "print(\"Out of\",len(eLgenerator),\"HDpolys that generated enclaves,\",nOK,\"had contiguous adjrs, while\",len(edgers),\n",
    "      \"neighbored a state boundary while\",len(internals),\"had only internal enclaves\")\n",
    " \n",
    "plotEnclaves = input(\"enter 1 to print the total enclave pops for all enclavy HDs.  This takes a few min; not required\")\n",
    "if str(plotEnclaves) == str(1):\n",
    "    for i,t in enumerate(internals): \n",
    "        if i%500 == 0:\n",
    "            print(\"computing full enclave lists for HD\",t)\n",
    "        fullEnclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        tEpop = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in fullEnclaveLists ] ) \n",
    "        plt.scatter(HDvPop[t],tEpop)\n",
    "    plt.plot([aDP,aDP],[0,5000],ls=\"--\")\n",
    "    print(\"Here are the total enclave pops for enclaving HDs not touching the state boundary vs. the original HD pop\")\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "05f929ce-4f94-47a6-baf4-60c5a89e2ae5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 807832 district pop vs 769364 target\n",
      "working on enclave-y HD 131 . We have evaluated 0 of 1395 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 4258 . We have evaluated 100 of 1395 enclavy HDs.Time is now 39\n",
      "working on enclave-y HD 4164 . We have evaluated 200 of 1395 enclavy HDs.Time is now 76\n",
      "working on enclave-y HD 683 . We have evaluated 300 of 1395 enclavy HDs.Time is now 100\n",
      "working on enclave-y HD 1849 . We have evaluated 400 of 1395 enclavy HDs.Time is now 143\n",
      "working on enclave-y HD 3130 . We have evaluated 500 of 1395 enclavy HDs.Time is now 171\n",
      "working on enclave-y HD 3775 . We have evaluated 600 of 1395 enclavy HDs.Time is now 197\n",
      "working on enclave-y HD 4718 . We have evaluated 700 of 1395 enclavy HDs.Time is now 214\n",
      "working on enclave-y HD 5053 . We have evaluated 800 of 1395 enclavy HDs.Time is now 229\n",
      "working on enclave-y HD 5569 . We have evaluated 900 of 1395 enclavy HDs.Time is now 255\n",
      "working on enclave-y HD 6649 . We have evaluated 1000 of 1395 enclavy HDs.Time is now 301\n",
      "working on enclave-y HD 1278 . We have evaluated 1100 of 1395 enclavy HDs.Time is now 343\n",
      "working on enclave-y HD 3542 . We have evaluated 1200 of 1395 enclavy HDs.Time is now 368\n",
      "working on enclave-y HD 5052 . We have evaluated 1300 of 1395 enclavy HDs.Time is now 388\n",
      "Out of 1395 HDs with enclaves 1395 had enclaves.\n",
      "Of these, 1370 wouldn't be over 807832 if all enclaves filled, while 25 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(internals + edgers):\n",
    "    if iii%100 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(internals+edgers),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(internals+edgers),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8987ad1f-d372-415d-a2ec-8fc6736c5102",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "222 1173 501 333\n",
      "25 25 1370\n"
     ]
    }
   ],
   "source": [
    "print(len(internals),len(edgers),len(doubleTroubleList),len(set(edgers).intersection(set(doubleTroubleList))))\n",
    "print(len(set(edgers).difference(set(canFill))) ,len(cantFill) , len(canFill))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "8fa887be-92e6-422a-8e80-e00fd3265aab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 0.92433 0.14535\n",
      "CCB cluster 0 = unit 6793 with pop 82874.0 now has use of 1.0065037212789716\n",
      "CCB cluster 1 = unit 6794 with pop 90352.0 now has use of 1.0003609873504258\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e9d50ef8-39b2-4e22-a11e-cabeda78721e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b759b3c7-b8a3-4d2c-a8fe-50fa93b3e940",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping\n",
    "HDunitList = [savedHDunitList[t].copy() for t in range(nHDs) ]   #restart\n",
    "HDvPop = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7729308-3cd8-4c12-9199-f400f59e6f2e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "551454fb-d57c-466f-8b31-39ccb2a728de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the HD centers for 46 cantFill HDs with border or big enclaves\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are the HD centers for\",len(cantFill),\"cantFill HDs with border or big enclaves\")\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "for t in cantFill:\n",
    "    plotPoly(hdCP[t].buffer(0.1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5269a224-4a97-417c-99af-6a98df0087ae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 8\n",
      "working on avg unit-to-HDcp dist for HD 1350\n",
      "working on avg unit-to-HDcp dist for HD 2488\n",
      "working on avg unit-to-HDcp dist for HD 3322\n",
      "working on avg unit-to-HDcp dist for HD 4173\n",
      "working on avg unit-to-HDcp dist for HD 5004\n",
      "working on avg unit-to-HDcp dist for HD 5827\n",
      "working on avg unit-to-HDcp dist for HD 6795\n"
     ]
    }
   ],
   "source": [
    "barredJettisonSet = set([6793, 6794] )  #lock in the clusters\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce01cce3-e408-4cb1-8761-0cc0be4a57c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "for u in range(allUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "6e2e44bd-a084-42f6-b533-5dc6d21912a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this code will solve large enclaves so HD and complement are contiguous for 25 HDs\n",
      "working on HD 123 cantFill 0 of 25 .Time is now 0\n",
      "working on HD 164 cantFill 20 of 25 .Time is now 6\n",
      "after the MustFree code run, we have the following for cluster usage\n",
      "current avg use and its sd are 0.92432 0.14533\n",
      "CCB cluster 0 = unit 6793 with pop 82874.0 now has use of 1.0065037212789716\n",
      "CCB cluster 1 = unit 6794 with pop 90352.0 now has use of 1.0003609873504258\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS IS THE MUSTFREE CODE - for high-pop HDs with map-border enclaves, can't fill; \n",
    "#  must jettison some inHD map-boundary units to connect the enclave to the larger complement\n",
    "#For each HD to be triaged, define the list(s) of contiguous enclave units that are on the map boundary, \n",
    "#  and the in-HD map-boundary segments.  ID which in-HD MBS's adjoin one vs. two enclaves.\n",
    "#     Fill all internal enclaves, and all boundary enclaves < 0.05 aDP.\n",
    "#   Then each loop, look for boundary enclaves that can be filled without overpopping.\n",
    "#     If none, pick the 1/2 has-1-boundary-enclave-neighbor that is least painful to jettison to free an enclave.\n",
    "#       (Jettison score based on pop-to-Free and distance from HDcP)\n",
    "#         Update HDpop, HDlist, and enclave & HD-boundary associations, then re-loop\n",
    "# Later, we will swell-fill to square up pop (w/ checkEnclave) biasing toward close-to-hdCP, underused\n",
    "borderSet = set(borderUnits)\n",
    "debug, debugPlot = False, False\n",
    "startTime = time.time()  #takes about 1.5sec per HD triage\n",
    "clusterJettisonBoost = 3.  #this discourages jettisoning of underused clusters\n",
    "print(\"this code will solve large enclaves so HD and complement are contiguous for\",len(cantFill),\"HDs\")\n",
    "nEnclavesPerHD = [0 for t in range(nHDs)]\n",
    "HDenclaveLists = [list() for t in range(nHDs)]\n",
    "for iii,t in enumerate(cantFill):\n",
    "    if iii %20 == 0:\n",
    "        print(\"working on HD\",t,\"cantFill\",iii,\"of\",len(cantFill),\".Time is now\",int(time.time() - startTime) )\n",
    "    shedUs = list()    \n",
    "    enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "    nEnclavesPerHD[t] = len(enclaveLists)\n",
    "    HDenclaveLists[t] = enclaveLists.copy()\n",
    "    if debug:\n",
    "        print(\"pre-adjust HDpop for HD\",t,\"is\",HDvPop[t],\"I found\",len(enclaveLists),\"TOTAL enclaves\")\n",
    "    internalEnclaves, edgeEnclaves, combinedEnclBorderList = list(), list(), list()\n",
    "    for jjj, eL in enumerate(enclaveLists.copy() ):\n",
    "        enclaveBorderList = list ( set(eL).intersection(borderSet) )\n",
    "        if debug:\n",
    "            print(\"In enclave\",jjj,\"I found\",len(enclaveBorderList),\"units that were enclaved and are in the map borderSet\")\n",
    "        combinedEnclBorderList += enclaveBorderList\n",
    "        ePop = np.sum( [unitPop[u] for u in eL] )\n",
    "        if len(enclaveBorderList) == 0 or ePop < 0.05 * aDP:  #internal or small enclave.  Fill it\n",
    "            if ePop > 0:  #in a prev treatment, could be an empty (surrounded) unit that we don't care about\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += ePop\n",
    "            internalEnclaves.append(eL)\n",
    "        else:\n",
    "            edgeEnclaves.append(eL)\n",
    "    if debug:\n",
    "        print(\"Of these\",len(internalEnclaves),\"were internal or small, so I filled them, leaving\",\n",
    "              len(edgeEnclaves),\"non-small border = edge enclaves\" )\n",
    "    if debugPlot:\n",
    "        print(\"Here is the original HD, internal enclaves ('x') and non-small border enclaves by enclave no\")\n",
    "        plotPoly(hdCP[t].buffer(0.3))\n",
    "        for u in HDunitList[t]:\n",
    "            if unitPop[u] > 0:\n",
    "                plotPoly(unitGeom[u],0.2)\n",
    "        for L in internalEnclaves:\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u],1.5)\n",
    "                    plotCenter(\"x\",unitCP[u],8)\n",
    "        for L in edgeEnclaves: #############\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u])\n",
    "                    #plotCenter(\"e\",unitCP[u],8)\n",
    "        #plotPoly(MAP,0.4)\n",
    "        #plt.show()\n",
    "    enclaveLists, combinedEnclBorderSet = list(), set(combinedEnclBorderList)\n",
    "    if len(edgeEnclaves) > 0:\n",
    "        # Now, we order by chain connectivity of HD and enclave sublists to be H0-E0-H1-E1 ... En-Hn+1\n",
    "        nonHDborderSet = borderSet.difference(  set(HDunitList[t]) )\n",
    "        nonHDlongBorderSet = nonHDborderSet.difference(combinedEnclBorderSet) #long=non HD boundary excluding enclaves\n",
    "        remainingEnclBorderSet = combinedEnclBorderSet.copy()\n",
    "        remainingHDborderSet =   borderSet.intersection(set(HDunitList[t]) )\n",
    "        #Now find the \"HDender\" unit which borders the non-enclave nonHD boundary, then find opp end of this HD bdry piece\n",
    "        HDender = list(set(getAdjoiners(nonHDlongBorderSet,unitNbrs)).intersection(remainingHDborderSet) )[0]\n",
    "        firstHDborderSet = getContigFromStarter(HDender,remainingHDborderSet,unitNbrs)\n",
    "        HDstarter = list(set(getAdjoiners(remainingEnclBorderSet,unitNbrs)).intersection(firstHDborderSet))[0]\n",
    "        HDborderSublists = [ list(firstHDborderSet) ]\n",
    "        unused = [1 for eL in edgeEnclaves]\n",
    "        eLadjoiners = [getAdjoiners(eL,unitNbrs) for eL in edgeEnclaves ]\n",
    "        for j in range(len(edgeEnclaves)): #find the enclave with the most HDstarter neighbors (at least 1)\n",
    "            found = False\n",
    "            for i,eL in enumerate(edgeEnclaves):\n",
    "                if unused[i] == 1:\n",
    "                    HDadjSet = set(HDborderSublists[j]).intersection(set(eLadjoiners[i]))\n",
    "                    if len(HDadjSet) > 0:\n",
    "                        unused[i] = 0\n",
    "                        eNo = i\n",
    "                        found = True\n",
    "                        remainingEnclBorderSet = remainingEnclBorderSet.difference(set(edgeEnclaves[eNo]) )\n",
    "                        enclaveLists.append(edgeEnclaves[eNo])\n",
    "                        remainingHDborderSet = remainingHDborderSet.difference(set(HDborderSublists[j]) )\n",
    "                        HDstarter = list(set(eLadjoiners[i]).intersection(remainingHDborderSet))[0]       \n",
    "                        HDborderSublists.append(getContigFromStarter(HDstarter,remainingHDborderSet,unitNbrs) )\n",
    "                        break\n",
    "                if found:\n",
    "                    break\n",
    "\n",
    "        enclavePops = [np.sum([unitPop[u] for u in L]) for L in enclaveLists]\n",
    "        #print(\"prior to adjustments, border enclavePops are\",enclavePops)         \n",
    "\n",
    "        nHDBS, nEnclaves = len(HDborderSublists), len(enclaveLists)\n",
    "        popToFree, freeingCounties, freeingUnits = [0. for n in range(nHDBS)], [list() for n in range(nHDBS) \n",
    "                                                                               ],[list() for n in range(nHDBS)]\n",
    "        for j,L in enumerate(HDborderSublists):\n",
    "            for u in L:\n",
    "                if int(allUnits[u]) == allUnits[u]:  #this border unit is a vtd\n",
    "                    c = countyNo[parentVTDno[allUnits[u]]]   #countyNo[allUnits[u]]\n",
    "                    if c not in freeingCounties[j]:\n",
    "                        if countyPop[c] < 0.1 * aDP:  #plan to add all in-county pop\n",
    "                            freeingCounties[j].append(c)\n",
    "                        else:\n",
    "                            popToFree[j] += unitPop[u]\n",
    "                            freeingUnits[j].append(u)\n",
    "                else: #border unit is a full county or cluster, or in a dense county.  Add the whole unit pop\n",
    "                    popToFree[j] += unitPop[u]\n",
    "                    freeingUnits[j].append(u)\n",
    "            for c in freeingCounties[j]:  #include ALL in-HD pop from each non-unit border county, not just its border units\n",
    "                #plotPoly(countyGeom[c],0.1)\n",
    "                for u in countyUnitList[c]:\n",
    "                    if u in HDunitList[t]:\n",
    "                        popToFree[j] += unitPop[u]\n",
    "                        freeingUnits[j].append(u)\n",
    "        freeingCP = [ getHDcp(  unitCP, unitPop, L) for L in freeingUnits ]\n",
    "        freeingScore = [ pTF/aDP - 0.5*freeingCP[j].distance(hdCP[t]) / avgDist[t] for j,pTF in enumerate(popToFree) ]\n",
    "        for j, fU in enumerate(freeingUnits):  #in above 0.5 is a fudgy\n",
    "            if len(barredJettisonSet.intersection(set(fU)) ) > 0: #has a cluster we want to hold onto\n",
    "                freeingScore[j] += clusterJettisonBoost #  ..so increase its score (make it harder to drop)\n",
    "        if debugPlot:\n",
    "            print(\"plotting the freeing units with negative numbers for each freeing group\")\n",
    "            for j,L in enumerate(freeingUnits):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        #plotPoly(unitGeom[u],0.5)\n",
    "                        plotCenter(\"-\"+str(j),unitGeom[u],6)\n",
    "                        pass\n",
    "            print(\"plotting the enclaveLists, with + numbers for each enclave\")\n",
    "            for j,L in enumerate(enclaveLists):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        plotPoly(unitGeom[u])\n",
    "                        plotCenter(j,unitCP[u],8)\n",
    "                        pass\n",
    "    \n",
    "    while len(enclaveLists) > 0:\n",
    "        if HDvPop[t] + np.min(enclavePops) < 1.05*aDP or np.min(enclavePops) < 0.05 * aDP:  #fill the smallest enclave\n",
    "            eNo = enclavePops.index(np.min(enclavePops))\n",
    "            HDunitList[t] += enclaveLists[eNo]\n",
    "            HDvPop[t] += enclavePops[eNo]\n",
    "            #print(t,\"=t. Absorbing enclave\",eNo,\"with pop\",enclavePops[eNo],\"HD total pop now\",int(HDfreePop[t]) )\n",
    "            enclaveBUs = list(set(enclaveLists[eNo]).intersection(set(borderUnits)) )\n",
    "            enclaveBpop = np.sum([unitPop[u] for u in enclaveBUs])\n",
    "            sNo, dropped_sNo = eNo, eNo+1\n",
    "            freeingScore[sNo] += freeingScore[sNo+1]+enclaveBpop/aDP\n",
    "            freeingUnits[sNo] += freeingUnits[sNo+1]+enclaveBUs\n",
    "            popToFree[sNo]    +=    popToFree[sNo+1]+enclaveBpop\n",
    "        else: #jettison one of the two end HD border lists; pick the less painful path to freedom\n",
    "            distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "            starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "            eNo, dropped_sNo = 0,0\n",
    "            if starterU not in freeingUnits[-1]:  #we can't drop the home unit, even to save an underused corner\n",
    "                if freeingScore[-1] < freeingScore[0] or starterU in freeingUnits[0]:\n",
    "                    eNo, dropped_sNo = len(enclaveLists)-1,len(enclaveLists)\n",
    "            barredIncluded0 = barredJettisonSet.intersection(set(freeingUnits[0]))\n",
    "            barredIncluded_1 = barredJettisonSet.intersection(set(freeingUnits[-1]))\n",
    "            #if len(barredIncluded0.union(barredIncluded_1)) > 0:\n",
    "            #    print(\"We are considering dropping an end unit to free from t\",t,\". Chosen, beg, end, scores are\")\n",
    "            #    print(dropped_sNo,barredIncluded0, barredIncluded_1, freeingScore[0], freeingScore[-1])\n",
    "            HDunitList[t] = list( set(HDunitList[t]).difference(set(freeingUnits[dropped_sNo]) ) )\n",
    "            HDvPop[t] -= popToFree[dropped_sNo]\n",
    "            #print(t,\"= t. Jettisoning HDborder\",dropped_sNo,\"with popToFree\",popToFree[dropped_sNo] )\n",
    "        del enclaveLists[eNo]\n",
    "        del enclavePops[eNo]\n",
    "        if debug:\n",
    "            print(t,\"= t. Now we have\",len(enclaveLists),\"left trapped.  Picked eNo, freeScores were\", eNo,freeingScore)\n",
    "        del freeingScore[dropped_sNo]\n",
    "        del freeingUnits[dropped_sNo]\n",
    "        del popToFree   [dropped_sNo] \n",
    "\n",
    "    #plotPoly(MAP,0.4)\n",
    "    if debugPlot:\n",
    "        plt.show()    \n",
    "        \n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t]*nDistricts\n",
    "print(\"after the MustFree code run, we have the following for cluster usage\")\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ca9ce3cb-c150-4f39-ae55-f781d39d7f57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "postMustFreeUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c7a8176-52b6-4727-b1f4-679cdf3b7dfb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c32dd108-073b-48aa-99ce-fea50c8d023c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([HDvPop[t] for t in popHDlist] )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f58fe2d0-3c62-412f-8984-ff5d67ea9b10",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After above work, re-classification of contiguity problems?\n",
      "working on HD 1500\n",
      "working on HD 2000\n",
      "working on HD 2500\n",
      "working on HD 3000\n",
      "working on HD 3500\n",
      "working on HD 4000\n",
      "working on HD 4500\n",
      "working on HD 5000\n",
      "working on HD 5500\n",
      "working on HD 6000\n",
      "working on HD 6500\n",
      "working on HD 7000\n",
      "out of 5979 total HDs, there were 5720 178 0 81 HDs that were clean, discontig only, enclave-only, both problems after triage\n"
     ]
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"After above work, re-classification of contiguity problems?\")\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "\n",
    "#40 5 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "74c4c39d-a238-4f10-81c4-949f67ea0d70",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing HDdiam for HD 300\n",
      "computing HDdiam for HD 600\n",
      "computing HDdiam for HD 1200\n",
      "computing HDdiam for HD 1500\n",
      "computing HDdiam for HD 2100\n",
      "computing HDdiam for HD 2400\n",
      "computing HDdiam for HD 2700\n",
      "computing HDdiam for HD 3000\n",
      "computing HDdiam for HD 3300\n",
      "computing HDdiam for HD 3600\n",
      "computing HDdiam for HD 3900\n",
      "computing HDdiam for HD 4200\n",
      "computing HDdiam for HD 4500\n",
      "computing HDdiam for HD 4800\n",
      "computing HDdiam for HD 5100\n",
      "computing HDdiam for HD 5400\n",
      "computing HDdiam for HD 5700\n",
      "computing HDdiam for HD 6000\n",
      "computing HDdiam for HD 6300\n",
      "computing HDdiam for HD 6600\n",
      "computing HDdiam for HD 6900\n"
     ]
    }
   ],
   "source": [
    "#optional HDpoly building\n",
    "HDpoly = [hdCP[t] for t in range(nHDs)]\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if t %300 == 0:\n",
    "        print(\"building HD shape from units for HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        HDpoly[t] = HDpoly[t].union(unitGeom[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "1ef683a2-4429-4a91-b1ea-a9b0950dea22",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDdiam = [0. for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    HDdiam[t] = HDpoly[t].area ** 0.5\n",
    "plt.hist([HDdiam[t] for t in popHDlist])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "45e3c17c-8c16-4bc5-b7fc-25b9250ed11a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we will regrow all disco'd HDs off by more than  38468 but less than 192341\n",
      "... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\n",
      "This is a total of 259 HDs to triage in this block\n",
      "working on swell-filling discontig HD 105 our 1 th HD we think we can swell out of 259\n",
      "working on swell-filling discontig HD 2005 our 41 th HD we think we can swell out of 259\n",
      "working on swell-filling discontig HD 3964 our 81 th HD we think we can swell out of 259\n",
      "working on swell-filling discontig HD 4690 our 121 th HD we think we can swell out of 259\n",
      "working on swell-filling discontig HD 5722 our 161 th HD we think we can swell out of 259\n",
      "working on swell-filling discontig HD 2093 our 201 th HD we think we can swell out of 259\n",
      "working on swell-filling discontig HD 4722 our 241 th HD we think we can swell out of 259\n",
      "we partially regrew 117 HDs, leaving 142 to regrow from scratch\n"
     ]
    }
   ],
   "source": [
    "### THIS IS THE CANSWELL CODE\n",
    "print(\"Here, we will regrow all disco'd HDs off by more than \",int(aDP-minPostFixPop),\"but less than\",int(0.25*aDP) )\n",
    "print(\"... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\")\n",
    "HDnAddedUnits = [0 for t in range(nHDs)]\n",
    "maxGap = 0.9 * np.median(unitPop)  #set a reasonable gap prior to picking the last block\n",
    "HDdiam = [HDpoly[t].area ** 0.5 for t in range(nHDs) ] #in case not defined before\n",
    "badDiscoList = list()\n",
    "nCanSwell = 0\n",
    "triageList = discontigOnly + bothProblems\n",
    "print(\"This is a total of\",len(triageList),\"HDs to triage in this block\")\n",
    " \n",
    "for i,t in enumerate(triageList): #canSwell\n",
    "    if i%40 == 0:\n",
    "        print(\"working on swell-filling discontig HD\",t,\"our\",i+1,\"th HD we think we can swell out of\",len(triageList) )\n",
    "    distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "    starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = getContigFromStarter(starterU, HDunitList[t], unitNbrs)\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    if contigPop < 0.75 * aDP or contigPop > 2.*aDP - minPostFixPop:  \n",
    "        badDiscoList.append(t)\n",
    "    else: #rebuild from this big piece\n",
    "        nCanSwell +=1\n",
    "        gap = aDP - contigPop\n",
    "        origGap = gap\n",
    "        currList, addedList = contigUs.copy(), list()\n",
    "        adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "        nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "        nearHDscore = [ (unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] for uu in adjoiners ] \n",
    "        stillGoing = True\n",
    "\n",
    "        while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "            while i < len(nearHDscore) and notYetPicked:        \n",
    "                listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "                canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "                if canAdd:\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't add any more units without creating an enclave\n",
    "            else: #we selected the best unit to add legally\n",
    "                gap -= unitPop[unitNoToAdd]\n",
    "                addedList.append(unitNoToAdd)  #so add it to our growing HD ...\n",
    "                currList.append( unitNoToAdd)\n",
    "                for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                    if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append((unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "                            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] )\n",
    "                del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                for i, uu in enumerate(nearHDlist.copy()):\n",
    "                    if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                        del nearHDscore[nearHDlist.index(uu)]\n",
    "                        del nearHDlist[ nearHDlist.index(uu)]\n",
    "        for u in addedList:\n",
    "            unitUse[u] += HDweight[t] * nDistricts\n",
    "        for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "            unitUse[u] -= HDweight[t] * nDistricts       \n",
    "        HDunitList[t] = contigUs + addedList\n",
    "        HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "        HDnAddedUnits[t] = len(addedList)\n",
    "    \n",
    "badDiscoList += enclaveOnly\n",
    "print(\"we partially regrew\",nCanSwell,\"HDs, leaving\",len(badDiscoList),\"to regrow from scratch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "f57325dd-523c-466a-80cb-8e10b739b4ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous avg use and its sd are 0.92432 0.14533\n",
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 0.92558 0.14297\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"previous avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "0ce918e1-0d30-4930-9fb8-0769d793683c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 8 time is now 0\n",
      "working on HD 1560 time is now 14\n",
      "working on HD 2899 time is now 25\n",
      "working on HD 3968 time is now 38\n",
      "working on HD 5004 time is now 51\n",
      "working on HD 6071 time is now 60\n",
      "out of 5979 total HDs, there were 5837.0 contiguous and 5885.0 complement-contiguous HDs\n",
      "50 HDs had both discontiguity problems, while enclave-only = 44 and discontig only= 92\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 142 44\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%1000 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "463db75a-694e-4c7d-9693-e10f3b4d65db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 730896 district pop vs 769364 target\n",
      "we'll also trim any HD with total islands' pop <= 15387\n",
      "working on HD 106\n",
      "working on HD 4667\n",
      "Out of 142 discontig HDs 142 had discontig HDs.\n",
      "Of these, 0 won't be under 730896 after all minor discontigys were shed, while 142 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "There are 142 HDs still with both discontinuity problems\n",
      "Additionally, 0 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%100 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for t in sPgenerator:    \n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "a1b8492c-6068-457c-aa00-0af663d85cfb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2-24-24 2nd pass - classify further: ID those with adjoiners intersecting the map boundary vs internal islands\n",
      "working on enclave-generating HD 105\n",
      "Out of 44 HDpolys that generated enclaves, 0 had contiguous adjrs, while 44 neighbored a state boundary while 0 had only internal enclaves\n"
     ]
    }
   ],
   "source": [
    "print(\"2-24-24 2nd pass - classify further: ID those with adjoiners intersecting the map boundary vs internal islands\")\n",
    "internals, edgers, nOK = list(), list(), 0\n",
    "for i,t in enumerate(eLgenerator):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on enclave-generating HD\",t)\n",
    "    twoNbrs = get2nbrs(HDunitList[t],unitNbrs)\n",
    "    isContig, adjSublist = isContiguous(twoNbrs,unitNbrs)\n",
    "    if isContig:\n",
    "        nOK +=1\n",
    "    else:\n",
    "        if len (set(getAdjoiners(HDunitList[t],unitNbrs)).intersection(set(borderUnits)))  > 0:\n",
    "            edgers.append(t)\n",
    "        else:\n",
    "            internals.append(t)\n",
    "print(\"Out of\",len(eLgenerator),\"HDpolys that generated enclaves,\",nOK,\"had contiguous adjrs, while\",len(edgers),\n",
    "      \"neighbored a state boundary while\",len(internals),\"had only internal enclaves\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "9d4b01b2-ccb6-4230-a040-960a2e247419",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 807832 district pop vs 769364 target\n",
      "working on enclave-y HD 105 . We have evaluated 0 of 44 enclavy HDs.Time is now 0\n",
      "Out of 44 HDs with enclaves 44 had enclaves.\n",
      "Of these, 44 wouldn't be over 807832 if all enclaves filled, while 0 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(internals + edgers):\n",
    "    if iii%100 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(internals+edgers),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(internals+edgers),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "85cd94ee-13a4-4125-a4c6-78007c881429",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([HDvPop[t] for t in popHDlist] )\n",
    "plt.show()\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "e78ad1ec-1f90-4d29-8cb1-9ad1415c2ade",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After above work, re-classification of contiguity problems?\n",
      "working on HD 1500\n",
      "working on HD 2000\n",
      "working on HD 2500\n",
      "working on HD 3000\n",
      "working on HD 3500\n",
      "working on HD 4000\n",
      "working on HD 4500\n",
      "working on HD 5000\n",
      "working on HD 5500\n",
      "working on HD 6000\n",
      "working on HD 6500\n",
      "working on HD 7000\n",
      "out of 5979 total HDs, there were 5837 92 0 50 HDs that were clean, discontig only, enclave-only, both problems after triage\n"
     ]
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"After above work, re-classification of contiguity problems?\")\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "39d37c15-759f-462c-b503-047b6b5e1554",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "92 50 142\n",
      "set()\n"
     ]
    }
   ],
   "source": [
    "print(len(discontigOnly),len(bothProblems), len(badDiscoList))\n",
    "print((set(discontigOnly).union(set(bothProblems)) ) .difference(set(badDiscoList)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e16dfee-b5bc-4863-8bed-9a6b22290aaa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "2e54bac4-c327-473d-bbb4-257486bddd20",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\n",
      "Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\n",
      "This takes about 5sec per HD :-( The total number of HDs to fix is 142\n",
      "swell-generating HD 106 our 1 th HD we must regrow out of 142 0 sec elapsed\n",
      "swell-generating HD 1890 our 21 th HD we must regrow out of 142 13 sec elapsed\n",
      "swell-generating HD 2129 our 41 th HD we must regrow out of 142 27 sec elapsed\n",
      "swell-generating HD 3313 our 61 th HD we must regrow out of 142 45 sec elapsed\n",
      "swell-generating HD 4088 our 81 th HD we must regrow out of 142 62 sec elapsed\n",
      "swell-generating HD 4667 our 101 th HD we must regrow out of 142 70 sec elapsed\n",
      "swell-generating HD 4866 our 121 th HD we must regrow out of 142 78 sec elapsed\n",
      "swell-generating HD 7159 our 141 th HD we must regrow out of 142 92 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "#THIS IS THE MUSTSPAWN code block\n",
    "print(\"And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\")\n",
    "print(\"Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\")\n",
    "print(\"This takes about 3sec per HD :-( The total number of HDs to fix is\",len(badDiscoList))\n",
    "avgDiam = MAP.area**0.5 / float(nDistricts)\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(badDiscoList):\n",
    "    if i%20 == 0:\n",
    "        print(\"swell-generating HD\",t,\"our\",i+1,\"th HD we must regrow out of\",len(badDiscoList),int(time.time()-startTime),\"sec elapsed\" )\n",
    "    notInCluster = True\n",
    "    for jj, geo in enumerate(CCBgeom):  #swell in-corner HDs from the corner cluster\n",
    "        if geo.contains(hdCP[t]):\n",
    "            starterU = allUnits.index(jj+0.25)\n",
    "            notInCluster = False\n",
    "    if notInCluster:\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = [starterU]  #we used getContigFromStarter(starterU, HDvtdList[t], unitNbrs) in above code block\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigUs.copy(), list()\n",
    "    adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "    nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "    nearHDscore = [ (0.2*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] for uu in adjoiners ]\n",
    "    stillGoing = True\n",
    "    \n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((0.1*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "                                                                        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] )\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts       \n",
    "    HDunitList[t] = contigUs + addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "b78199da-ac7c-4cfb-ac0b-26363a8a8c56",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prev avg use and its sd are 0.92558 0.14297\n",
      "defining and displaying current unit use after most recent manipulations; compare to original farther above.\n",
      "current avg use and its sd are 0.92665 0.1377\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"prev avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after most recent manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "a2242181-3a07-40b7-ae56-bbe47bd2dc79",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final check -- any more disco's ?\n",
      "working on HD 8\n",
      "working on HD 778\n",
      "working on HD 1560\n",
      "working on HD 2385\n",
      "working on HD 2899\n",
      "working on HD 3435\n",
      "working on HD 3968\n",
      "working on HD 4475\n",
      "working on HD 5004\n",
      "working on HD 5525\n",
      "working on HD 6071\n",
      "working on HD 6686\n",
      "out of 5979 total HDs, there were 5905 0 74 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 306 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",time.time() - startTime,\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)\n",
    "#3-2-24 FL timing 5-> 214s, 368; 4 --> 130s, 4xx; 3-> 76s, 470; 6 -> 329s, 275 ; 2 with full check --> 810sec, only 2 discontig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de4d8af8-cc64-4215-9b95-f4c6a2bd03af",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "750fb250-e608-477a-924e-b21882d3d060",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "above should have cleaned them all up, but it didnt, so we will run the MUSTFREE code again\n",
      "this code will solve large enclaves so HD and complement are contiguous for 0 HDs\n",
      "working on HD 106 enclaveOnly 0 of 74 .Time is now 0\n",
      "pre-adjust HDpop for HD 106 is 769859.4982352357 I found 3 TOTAL enclaves\n",
      "Of these 3 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 109 is 770473.076624329 I found 3 TOTAL enclaves\n",
      "Of these 3 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 268 is 769264.1607958153 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 293 is 767512.0 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 405 is 769550.1178091656 I found 6 TOTAL enclaves\n",
      "Of these 6 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 411 is 769986.1300075904 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 635 is 768653.336812082 I found 3 TOTAL enclaves\n",
      "Of these 3 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 665 is 769251.2788651137 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 669 is 768768.7368958987 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 690 is 771428.7074774625 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "working on HD 1157 enclaveOnly 10 of 74 .Time is now 0\n",
      "pre-adjust HDpop for HD 1157 is 767184.0099005698 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 1169 is 769931.4152449573 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 1863 is 769721.8307773587 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 1890 is 768303.0241081656 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 1935 is 769516.0317369484 I found 3 TOTAL enclaves\n",
      "Of these 3 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 1937 is 768667.9829330628 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 1966 is 768984.8314080422 I found 5 TOTAL enclaves\n",
      "Of these 5 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 1975 is 770618.0522545297 I found 3 TOTAL enclaves\n",
      "Of these 3 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 1989 is 768729.0522545297 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 1992 is 767246.5880596773 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "working on HD 1999 enclaveOnly 20 of 74 .Time is now 0\n",
      "pre-adjust HDpop for HD 1999 is 767597.2670999817 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 2001 is 767651.4939645524 I found 3 TOTAL enclaves\n",
      "Of these 3 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 2003 is 770179.6248013633 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 2005 is 771306.6135142647 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 2008 is 771121.6135142646 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 2016 is 770712.4939645524 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 2017 is 767203.6135142647 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 2019 is 770712.4939645524 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 2027 is 768703.4226671536 I found 7 TOTAL enclaves\n",
      "Of these 7 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 2093 is 767619.4459120935 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "working on HD 2135 enclaveOnly 30 of 74 .Time is now 0\n",
      "pre-adjust HDpop for HD 2135 is 768803.5187882548 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 2141 is 767956.3620139083 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 2167 is 767445.5877586951 I found 4 TOTAL enclaves\n",
      "Of these 4 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 2169 is 767135.6135142647 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 2187 is 767464.9981495598 I found 4 TOTAL enclaves\n",
      "Of these 4 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 2614 is 768921.373297219 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 3270 is 771161.8314172864 I found 3 TOTAL enclaves\n",
      "Of these 3 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 3273 is 770192.455178417 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 3274 is 767184.3751233164 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 3275 is 769822.0521876831 I found 4 TOTAL enclaves\n",
      "Of these 4 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "working on HD 3308 enclaveOnly 40 of 74 .Time is now 0\n",
      "pre-adjust HDpop for HD 3308 is 770649.5346437874 I found 3 TOTAL enclaves\n",
      "Of these 3 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 3313 is 767147.5205932125 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 3341 is 769054.0265965882 I found 3 TOTAL enclaves\n",
      "Of these 3 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 3350 is 767834.6900928176 I found 4 TOTAL enclaves\n",
      "Of these 4 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 3356 is 767433.5762044868 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 3394 is 767899.0481219683 I found 3 TOTAL enclaves\n",
      "Of these 3 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 3403 is 769657.8048332534 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 3404 is 770159.5833246713 I found 5 TOTAL enclaves\n",
      "Of these 5 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 3467 is 767373.5140239452 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 3817 is 769428.2861689319 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "working on HD 3818 enclaveOnly 50 of 74 .Time is now 0\n",
      "pre-adjust HDpop for HD 3818 is 767447.4393475156 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 3976 is 768894.678788976 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 4100 is 770425.5321262015 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 4359 is 769506.1455178424 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 4361 is 771340.4495007633 I found 3 TOTAL enclaves\n",
      "Of these 3 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 4445 is 770138.2582466693 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 4654 is 768976.0 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 4684 is 767691.5613241219 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 4691 is 770797.5613241219 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 4731 is 768160.2183157194 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "working on HD 4778 enclaveOnly 60 of 74 .Time is now 0\n",
      "pre-adjust HDpop for HD 4778 is 768484.329623288 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 4854 is 768833.5613241219 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 4866 is 768408.9073091175 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 5002 is 767846.5613241219 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 5005 is 770643.5613241219 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 5114 is 769142.5613241219 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 5119 is 769528.5613241219 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 5123 is 768244.0 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 5165 is 769775.7195367248 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 6070 is 767324.3378504268 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "working on HD 7054 enclaveOnly 70 of 74 .Time is now 0\n",
      "pre-adjust HDpop for HD 7054 is 767359.7672536767 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 7091 is 768276.2047996155 I found 3 TOTAL enclaves\n",
      "Of these 3 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 7159 is 770226.5176986292 I found 2 TOTAL enclaves\n",
      "Of these 2 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "pre-adjust HDpop for HD 7167 is 768369.1370135375 I found 1 TOTAL enclaves\n",
      "Of these 1 were internal or small, so I filled them, leaving 0 non-small border enclaves\n",
      "after the MustFree code run, we have the following for cluster usage\n",
      "current avg use and its sd are 0.92672 0.13773\n",
      "CCB cluster 0 = unit 6793 with pop 82874.0 now has use of 0.9733880685005182\n",
      "CCB cluster 1 = unit 6794 with pop 90352.0 now has use of 0.9960002441574092\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"above should have cleaned them all up, but it didnt, so we will run the MUSTFREE code again\") #RUN ONLY IF NEEDED\n",
    "\n",
    "#THIS IS THE MUSTFREE CODE - for high-pop HDs with map-border enclaves, can't fill; \n",
    "#  must jettison some inHD map-boundary units to connect the enclave to the larger complement\n",
    "#For each HD to be triaged, define the list(s) of contiguous enclave units that are on the map boundary, \n",
    "#  and the in-HD map-boundary segments.  ID which in-HD MBS's adjoin one vs. two enclaves.\n",
    "#     Fill all internal enclaves, and all boundary enclaves < 0.05 aDP.\n",
    "#   Then each loop, look for boundary enclaves that can be filled without overpopping.\n",
    "#     If none, pick the 1/2 has-1-boundary-enclave-neighbor that is least painful to jettison to free an enclave.\n",
    "#       (Jettison score based on pop-to-Free and distance from HDcP)\n",
    "#         Update HDpop, HDlist, and enclave & HD-boundary associations, then re-loop\n",
    "# Later, we will swell-fill to square up pop (w/ checkEnclave) biasing toward close-to-hdCP, underused\n",
    "borderSet = set(borderUnits)\n",
    "debug, debug2 = True, False\n",
    "startTime = time.time()  #takes about 1.5sec per HD triage\n",
    "clusterJettisonBoost = 3.  #this discourages jettisoning of underused clusters\n",
    "print(\"this code will solve large enclaves so HD and complement are contiguous for\",len(cantFill),\"HDs\")\n",
    "for iii,t in enumerate(enclaveOnly):#cantFill\n",
    "    if iii %10 == 0:\n",
    "        print(\"working on HD\",t,\"enclaveOnly\",iii,\"of\",len(enclaveOnly),\".Time is now\",int(time.time() - startTime) )\n",
    "    shedUs = list()    \n",
    "    enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "    if debug:\n",
    "        print(\"pre-adjust HDpop for HD\",t,\"is\",HDvPop[t],\"I found\",len(enclaveLists),\"TOTAL enclaves\")\n",
    "    internalEnclaves, edgeEnclaves, combinedEnclBorderList = list(), list(), list()\n",
    "    for eL in enclaveLists.copy():\n",
    "        enclaveBorderList = list ( set(eL).intersection(borderSet) )\n",
    "        combinedEnclBorderList += enclaveBorderList\n",
    "        ePop = np.sum( [unitPop[u] for u in eL] )\n",
    "        if len(enclaveBorderList) == 0 or ePop < 0.05 * aDP:  #internal or small enclave.  Fill it\n",
    "            if ePop > 0:  #in a prev treatment, could be an empty (surrounded) unit that we don't care about\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += ePop\n",
    "            internalEnclaves.append(eL)\n",
    "        else:\n",
    "            edgeEnclaves.append(eL)\n",
    "    if debug:\n",
    "        print(\"Of these\",len(internalEnclaves),\"were internal or small, so I filled them, leaving\",\n",
    "              len(edgeEnclaves),\"non-small border enclaves\" )\n",
    "    if debug2:\n",
    "        print(\"Here is the original HD, internal enclaves ('x') and non-small border enclaves by enclave no\")\n",
    "        plotPoly(hdCP[t].buffer(0.3))\n",
    "        for u in HDunitList[t]:\n",
    "            if unitPop[u] > 0:\n",
    "                plotPoly(unitGeom[u],0.2)\n",
    "        for L in internalEnclaves:\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u],1.5)\n",
    "                    plotCenter(\"x\",unitCP[u],8)\n",
    "        for L in edgeEnclaves: #############\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u])\n",
    "                    #plotCenter(\"e\",unitCP[u],8)\n",
    "        #plotPoly(MAP,0.4)\n",
    "        #plt.show()\n",
    "    enclaveLists, combinedEnclBorderSet = list(), set(combinedEnclBorderList)\n",
    "    if len(edgeEnclaves) > 0:\n",
    "        # Now, we order by chain connectivity of HD and enclave sublists to be H0-E0-H1-E1 ... En-Hn+1\n",
    "        nonHDborderSet = borderSet.difference(  set(HDunitList[t]) )\n",
    "        nonHDlongBorderSet = nonHDborderSet.difference(combinedEnclBorderSet) #long=non HD boundary excluding enclaves\n",
    "        remainingEnclBorderSet = combinedEnclBorderSet.copy()\n",
    "        remainingHDborderSet =   borderSet.intersection(set(HDunitList[t]) )\n",
    "        #Now find the \"HDender\" unit which borders the non-enclave nonHD boundary, then find oop end of this HD bdry piece\n",
    "        HDender = list(set(getAdjoiners(nonHDlongBorderSet,unitNbrs)).intersection(remainingHDborderSet) )[0]\n",
    "        firstHDborderSet = getContigFromStarter(HDender,remainingHDborderSet,unitNbrs)\n",
    "        HDstarter = list(set(getAdjoiners(remainingEnclBorderSet,unitNbrs)).intersection(firstHDborderSet))[0]\n",
    "        HDborderSublists = [ list(firstHDborderSet) ]\n",
    "        unused = [1 for eL in edgeEnclaves]\n",
    "        eLadjoiners = [getAdjoiners(eL,unitNbrs) for eL in edgeEnclaves ]\n",
    "        for j in range(len(edgeEnclaves)): #find the enclave with the most HDstarter neighbors (at least 1)\n",
    "            found = False\n",
    "            for i,eL in enumerate(edgeEnclaves):\n",
    "                if unused[i] == 1:\n",
    "                    HDadjSet = set(HDborderSublists[j]).intersection(set(eLadjoiners[i]))\n",
    "                    if len(HDadjSet) > 0:\n",
    "                        unused[i] = 0\n",
    "                        eNo = i\n",
    "                        found = True\n",
    "                        remainingEnclBorderSet = remainingEnclBorderSet.difference(set(edgeEnclaves[eNo]) )\n",
    "                        enclaveLists.append(edgeEnclaves[eNo])\n",
    "                        remainingHDborderSet = remainingHDborderSet.difference(set(HDborderSublists[j]) )\n",
    "                        HDstarter = list(set(eLadjoiners[i]).intersection(remainingHDborderSet))[0]       \n",
    "                        HDborderSublists.append(getContigFromStarter(HDstarter,remainingHDborderSet,unitNbrs) )\n",
    "                        break\n",
    "                if found:\n",
    "                    break\n",
    "\n",
    "        enclavePops = [np.sum([unitPop[u] for u in L]) for L in enclaveLists]\n",
    "        #print(\"prior to adjustments, border enclavePops are\",enclavePops)         \n",
    "\n",
    "        nHDBS, nEnclaves = len(HDborderSublists), len(enclaveLists)\n",
    "        popToFree, freeingCounties, freeingUnits = [0. for n in range(nHDBS)], [list() for n in range(nHDBS) \n",
    "                                                                               ],[list() for n in range(nHDBS)]\n",
    "        for j,L in enumerate(HDborderSublists):\n",
    "            for u in L:\n",
    "                if int(allUnits[u]) == allUnits[u]:  #this border unit is a vtd\n",
    "                    c = countyNo[allUnits[u]]\n",
    "                    if c not in freeingCounties[j]:\n",
    "                        if countyPop[c] < 0.1 * aDP:  #plan to add all in-county pop\n",
    "                            freeingCounties[j].append(c)\n",
    "                        else:\n",
    "                            popToFree[j] += unitPop[u]\n",
    "                            freeingUnits[j].append(u)\n",
    "                else: #border unit is a full county or cluster, or in a dense county.  Add the whole unit pop\n",
    "                    popToFree[j] += unitPop[u]\n",
    "                    freeingUnits[j].append(u)\n",
    "            for c in freeingCounties[j]:  #include ALL in-HD pop from each non-unit border county, not just its border units\n",
    "                #plotPoly(countyGeom[c],0.1)\n",
    "                for u in countyUnitList[c]:\n",
    "                    if u in HDunitList[t]:\n",
    "                        popToFree[j] += unitPop[u]\n",
    "                        freeingUnits[j].append(u)\n",
    "        freeingCP = [ getHDcp(  unitCP, unitPop, L) for L in freeingUnits ]\n",
    "        freeingScore = [ pTF/aDP - 0.5*freeingCP[j].distance(hdCP[t]) / avgDist[t] for j,pTF in enumerate(popToFree) ]\n",
    "        for j, fU in enumerate(freeingUnits):  #in above 0.5 is a fudgy\n",
    "            if len(barredJettisonSet.intersection(set(fU)) ) > 0: #has a cluster we want to hold onto\n",
    "                freeingScore[j] += clusterJettisonBoost #  ..so increase its score (make it harder to drop)\n",
    "        if debug2:\n",
    "            print(\"plotting the freeing units with negative numbers for each freeing group\")\n",
    "            for j,L in enumerate(freeingUnits):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        #plotPoly(unitGeom[u],0.5)\n",
    "                        plotCenter(\"-\"+str(j),unitGeom[u],6)\n",
    "                        pass\n",
    "            print(\"plotting the enclaveLists, with + numbers for each enclave\")\n",
    "            for j,L in enumerate(enclaveLists):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        plotPoly(unitGeom[u])\n",
    "                        plotCenter(j,unitCP[u],8)\n",
    "                        pass\n",
    "    \n",
    "    while len(enclaveLists) > 0:\n",
    "        if HDvPop[t] + np.min(enclavePops) < 1.05*aDP or np.min(enclavePops) < 0.05 * aDP:  #fill the smallest enclave\n",
    "            eNo = enclavePops.index(np.min(enclavePops))\n",
    "            HDunitList[t] += enclaveLists[eNo]\n",
    "            HDvPop[t] += enclavePops[eNo]\n",
    "            #print(t,\"=t. Absorbing enclave\",eNo,\"with pop\",enclavePops[eNo],\"HD total pop now\",int(HDfreePop[t]) )\n",
    "            enclaveBUs = list(set(enclaveLists[eNo]).intersection(set(borderUnits)) )\n",
    "            enclaveBpop = np.sum([unitPop[u] for u in enclaveBUs])\n",
    "            sNo, dropped_sNo = eNo, eNo+1\n",
    "            freeingScore[sNo] += freeingScore[sNo+1]+enclaveBpop/aDP\n",
    "            freeingUnits[sNo] += freeingUnits[sNo+1]+enclaveBUs\n",
    "            popToFree[sNo]    +=    popToFree[sNo+1]+enclaveBpop\n",
    "        else: #jettison one of the two end HD border lists; pick the less painful path to freedom\n",
    "            distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "            starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "            eNo, dropped_sNo = 0,0\n",
    "            if starterU not in freeingUnits[-1]:  #we can't drop the home unit, even to save an underused corner\n",
    "                if freeingScore[-1] < freeingScore[0] or starterU in freeingUnits[0]:\n",
    "                    eNo, dropped_sNo = len(enclaveLists)-1,len(enclaveLists)\n",
    "            barredIncluded0 = barredJettisonSet.intersection(set(freeingUnits[0]))\n",
    "            barredIncluded_1 = barredJettisonSet.intersection(set(freeingUnits[-1]))\n",
    "            #if len(barredIncluded0.union(barredIncluded_1)) > 0:\n",
    "            #    print(\"We are considering dropping an end unit to free from t\",t,\". Chosen, beg, end, scores are\")\n",
    "            #    print(dropped_sNo,barredIncluded0, barredIncluded_1, freeingScore[0], freeingScore[-1])\n",
    "            HDunitList[t] = list( set(HDunitList[t]).difference(set(freeingUnits[dropped_sNo]) ) )\n",
    "            HDvPop[t] -= popToFree[dropped_sNo]\n",
    "            #print(t,\"= t. Jettisoning HDborder\",dropped_sNo,\"with popToFree\",popToFree[dropped_sNo] )\n",
    "        del enclaveLists[eNo]\n",
    "        del enclavePops[eNo]\n",
    "        if debug2:\n",
    "            print(t,\"= t. Now we have\",len(enclaveLists),\"left trapped.  Picked eNo, freeScores were\", eNo,freeingScore)\n",
    "        del freeingScore[dropped_sNo]\n",
    "        del freeingUnits[dropped_sNo]\n",
    "        del popToFree   [dropped_sNo] \n",
    "\n",
    "    #plotPoly(MAP,0.4)\n",
    "    if debug2:\n",
    "        plt.show()    \n",
    "        \n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t]*nDistricts\n",
    "print(\"after the MustFree code run, we have the following for cluster usage\")\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "f35a5cc8-17f0-4aac-b4dd-c37ef7501fc9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "635 True False t,unbroken, noEnclave\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "669 True False t,unbroken, noEnclave\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1935 True False t,unbroken, noEnclave\n"
     ]
    },
    {
     "data": {
      "image/png": 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HExYWxqpVq1rO2e121q9fz4gRI055//Dhw1vdA7By5crT3nMlU2s09Jt8Dfe89SHDbrgZjV5PaeZhvv7T//H9P16itrS4q5sonINB182ix6hxyC4X37/+EoUH9nV1kwRBEC4KHQpYnnrqKdLS0sjPz2f//v08/fTTrFu3jttuuw1Jknj44Yd56aWXWLx4MQcOHGDevHl4eHhw6623tpRxxx138OSTT7Z8/9BDD7Fy5UpeffVVDh8+zKuvvsrq1at5+OGHO+0hL0c6owcjZ8/lnjc/pPeEyUiSiqztm/nksfv5+aP3RU6PS5QkSUz57cMkDhqKy+Hgu9deoDTrSFc3SxAEoct1aFnzPffcw88//0xpaSm+vr706dOHxx9/nKuucq9wUBSF5557jg8++IDa2lqGDh3Ku+++S2pqaksZ48aNIy4ujk8++aTl2LfffsszzzxDbm4uiYmJ/PWvf2XWrFkdepDLaVnz2agqzGfD/E/I27MTAJ3RyJDpNzHgmuvR6g1d3Dqho5x2O4tffY7CA+nojB5M/8MzxKT26epmCYIgdLr2vn+L1PyXmcID6az/4iMq8nIA8AoIZOTsufQcO0Gk+r/EOKxWFr3yF4oyDqDWaLjlhb8TmtCtq5slCILQqUTAcgVTZJnDm9aT9vVnNFRVAhAUE8eY2+4iru+AM67AEi4eTrud719/ibw9OwmIjOa2v/4DndGjq5slCILQaUTAIuC029mz4ge2Lf4GW5M72VxM736MnXs3IXEJXdw6ob3M9SY+++ODNNXWENG9B9c+9Ed8goK7ulmCIAidQgQsQgtLQz3bFn/Dnp9+bEn133PUOEbefDs+QSFd3TyhHUqzj/Dti89gt1jQGoyMuPEW+l99PWqNpqubJgiCcE5EwCKcxFRRRtpXn3Fk8wYA1FotA66+niEzbsLg6dXFrRPOpLashOXvvk5p5mEAgmPimPH4n0VviyAIlzQRsAinVJadyfovP6Lo0AEADN4+DJ81h76Tr0Gt0XZx64TTUWSZA+tXk/blJ1ga6vENDePWF/+Bh49vVzdNEAThrIiARTgtRVHI3b2dDV9+Qk2xe1sDv9BwRt1yJ92HjRQTcy9y9VWV/O+5JzBVlBOT2pcbnnoelVqsAhME4dIjAhahXWSXiwNrV7Hpf19gNtUBEN4tmTG3301USq+ubZxwWtVFhXz51KM4bFZG3XwHQ2fO7uomCYIgdJgIWIQOsVst7Fy6iB1LF+G02QBIHDSMMbfNIyAiqotbJ5zKwfU/89O/3kCl1jD35TcIjo3v6iYJgiB0iAhYhLPSWFvDlgXz2b9mJYoiI6lU9Jk4leE33oKnn39XN0/4BUVRWPL3F8nZuQ3/8EjmvvyGyNMiCMIlRQQswjmpLipkw/xPyN21HQCtwcjg62cx6NqZaA0i1f/FxFxv4vMnHqKxuorYPv2Z/tjT4r+RIAiXDBGwCJ3i6MF9rP/iY8pzswDw9A9gxE23kTp+kkj1fxEpyTzMghefxmmzEZPahxueekFMwhUE4ZIgAhah0yiyzJEtaaR99Rn1leUABEbFMGbuXcT3GyRWFLWlqQpqCyByAFyg16ckM4Nv//onHFYLQ2fOYdTNt1+QegVBEM6FCFiETud0ONi74ge2LfoGa1MjADGpfRhz292XxaZ8sqyw52gdsYEeBHnpz62wvfMhOBkaK8AYANFDLkjgcnjTen58+zVUajW/+eBzjN7iZ0EQhItbe9+/VRewTcIlTqPVMmjaTO55+z8Mum4Wao2GwgP7+OLJh1n2zt+pr6zo6iZ2mEtW2Hu0jjWHy1mfWUlMgAcHik3tL6AqC9qK+R0Wdw9LSA/Qe0POGvfHeZYycizBcQnILhdZ2zef9/oEQRAuFLERidBhBi8vxs69m36Tr2Xj159xeNN6MjauI3PrRvpffT1DZ8zG4HVyqn9ZduG02XDYbDjtNpx2u/vr5u/bciwUkE74Xu/hSUT3lLNuv90psy2vGqdLQaWSSI3woV+030l1nlZ1DlRlugOTsn0QMwKMflB5GJqqwTMIVBrI+AFq88E7DIK6n3WbOyJlxBgq83M5snkDfSZOvSB1CoIgnG8iYLmCuJxOmupqUWQXiqygoKDICjR/VhQZFAXlhA8UBafDgcNmBVluVZ4C9BwzgYikFPavXUllQR47ly4ifdVyeowcQ8KAIaiaN+eTAFQqtDo9WoMBjU6H1mDEw9cPjV6PRqtr91yYivxcKvJzO7zjdE2TnV0FtWjUEsMTAjFoz2FSqsHPHaykTAO1Fkr3QnW2O1BJmnT8OlsjFG4BSQXdJp59fR3QbfAw0uZ/QvHhQxekPkEQhAtBBCwXEXcwISPLxwIHGUVWkGX5eDDRfP5YMKE0f91yjazgcjmRXS73UMUJQYBKrcbTzx+VWoMkgSSpkNQqJElCUkmA+7OEhKRyjxZKkoRaq0Or15961Um/gfSbOo28PTvZ8OXHVBcVsu/nFRTs38uom+8gefjolvI6Q0hcAiWZGZTlZBGWmHTG60tNFvYVmQjw1DGpR0i7AiNFUU5/nWegO1jJ+B6SJkNE/7avK94JGr27N8ZS5+6FOc88fN11uJxOXE6H2B9KEITLgph0exE5vGk9DpuN8KRkJJXKHVCoJCRJhUqlgubAQpKOBRmq5utOuEYloVKpuuxNSna5OLBuNZsXfElTbQ0AYYlJjJl7N9E9e3dqXTUlRdSVlRLfv+2VSvlVTWSWNxDma6BPlF+7y80orcffQ0eYbztymSgKZCyFnte3fd5hcQ8Lefi7u6S6TTzvk29dTidv3jYDgPv/M19MvBUE4aImVgldoiryc3E5HYR3S+7qppwTh9XKzh8Xs+P7RTisFgASBg5hzK13ERgV3Wn1WJsaydu7i26DhqLVuwOMw2X1FFabiQvypHuod8fb7pLZllvDqKSgM15rs1UgZa9B1+vmMxfcVO0eOroAQ0NvzZ2F02Hn7rc+xD8s4rzXJwiCcLZEwHIJq8jPRXY6Cet2YSZpnk9NdbVs+fYr9v38E4rsTvXfe8JkRtx0W6el+ldkmeydW8mXAlA8/EgJ8yEm8NzS0689UsH45JBWx6zWEhobjwAKkuQeHtPpgqE0Hbk2F4/us9H6n2GIKm+Du9el+5Rzat+ZfP7EQ1Tk5XDdo0/SfejI81qXIAjCuWjv+7eYw3IRColLoDw3u91zNC5mnn7+TPrV/fS/+jrS5n9Kzs6t7Fv9Exlp6xh03SwGXTcTncF41uU7XU5Wbt9Bk8VGocpOtDfnHKz8Uk3NZhTFgcVajL/fEDw9f5FzxrsniizTcOg/WEo34tPzrrYLOvjd8Qm40UPAeP72Zgrv1p2KvBzWffYfAqNiCIzsvF4tQRCEriB6WC5i5bnZAJdFUrZjijIOsP6LjyjLzgTcAY071f9V7U4lX2WxsPtIFo11tciKzNiBgwj2cQ/9rCmsYW+ZiUcGx51TBt7le/cwJLoBgLq6HSQmPtau+6xFG7AUr8d30BOo1KeYR6QocGAhpN5w3uazNNRUseD5p6ktLcbg7cPtL7+JT3DImW8UBEG4wMSQ0GWiPDcbRZYvi+GhYxRFIXPrRtK++hRTeRkAAZHRjLltHgkDhrQKNIobmkjPysLWWA+AhISXXku/pCSCAgLbLD/PZOHj9CKeGZGITnN2q5PW7t9A/4QB+Hl6UXj0Y4KDJmM0Rp7xPlmW2bX1WoIaNAQGj28+qkIXMx5DUN/jF9oaIH8TJJ+/PCnmehMLXniaqsJ8ht94CyNuuu281SUIgnC2RMByGakqzKextobYPv0vq317nA4H+1YtY8vCr7E2unszonqmMvrWeewqq8TmchHs6UH/pO74+Pl1qOxqs51/bM/jL6OSzipoqWkoJ72gkPGpg5FlB/n575KQ8PBp78nJfR1J0uLp2Y3AgPFoNO5JwIrspClnMc6qA/iowlEFdHMnpmkoh763QCcu+f6lA2tXseL9t/ANDePOv/1T7OIsCMJFRwQslxlLQz3Fhw+h0WqJTOl1Wb3xWJsa2f7dAnYv/x6XwwFA/JARTLz9bnxDws663Dqrg79tzeVPo5IwnEXQsnDrCm4Y5p4cm5v7BnFxD6JSaVAUBau1mNrazdjslS2p+XW6QCIjbzllebKjkfotz+M36pXzGqScyGZu4pPH7qexppo+E6dy1a9/d0HqFQRBaC8RsFymXE4HxYcP4bDZ0Bs9CO+eglpz6c+d3l9cTPr2rUiZBynbswMUBbVGQ78p0xg6aw5Gr44vTwZosDl4eUsuz4xMxEPbsddpRfpmBickEuAdisVSTFHR52g0XiiKC70+FH//4RiNUS0rhs7E5bJSW7uZoKAJZ/MoZ63wQDoLXnwGFIXr//A0SYOHX9D6BUEQTkcELFcAu8VMSeZhXE4ngZHR+IWFd3WTOqzJ7uDj5csZ3i2Rgb16AVCel8OGLz6i8EA6AHpPT4bOnEP/KdPQ6HQdrqPR7uSlzTk8NTIRrw4ELU6XzPc7VjFrWOcsQbZaS6lv2IeHMQ4vrwubZ2f9Fx+xc+kiDF7e3P7q2/gEBV/Q+gVBEE5FBCxXmIr8XBxWK5EpPbu6KR1idzhZsX07140c0eq4oijkp+9mw5cfU1WYD4BPcAijbr6DlBFjOpzqv8nu4q+bsnliRAI++vZnAf5h1zrG9xyAp7Hz/k1ZLMU0NblXSel0QXh7p573uUkup4P5z/yBirwcAqNiuPn5v2HwPHmDSkEQhAtNBCxXoLLsTJpMdYQmdMPLP6Crm9NuS9LSmD56dJvnZNnFofVr2PTN5zQ2p/oPTejGmNvuJia1T4fqsThcvLApm8eHJeBraF/QYnc6WL57HdOHXNWhutrLZqugofEQKAo6XRA+Pp27fcGJ6isrmP/sH2iqrSG6Z29mPfU8Gq3YZ0gQhK4lApYrlKIoVOTl0GSqBUCt0RISl3BR7yfzXVoaM04RsBzjsFnZ9eMSdnz/LXZLc6r/AYMZfes8gqJj212Xzeni+Y05/GFYPP7tDFq+27aaqweMRq/Vt7ues9HQcAirrZQA/5Go1ednUnVFfi5f//lxHFYLiYOGcvUDj6H36NxEe4IgCB0hAhYBcC8drizIxdJQjyK7J7JKKhUoEJ3aG5WqfRNGz6fT9bD8ktlUx5aFX7Fv9U/ILheSpCJ1/CRG3HQbXqfIy/JLdpfM8xuzeWRIPIHGk4OWeouFQ0U5VJrKAbDanfh7eTKpz6j2P9RZcrnMVFWvx8c7FaPx/GSnzd+3h8WvPIfscuIfHsl1jz5JcEzcealLEAThTETAIrRJkWVkWaaptobS7CMEREQRHBvfpW3qSMByTE1JMRu/+pSs7ZsB0Oj1DJo2k8HXzUJnPHOPgdMl88d1h5gYZMXlqGs5LiHhqdOQEhlDeEAsknRhlh+fyOWytuR9ae8KpI4qyTzMD2++SkN1JRqdnkm/up9eY8//poyCIAi/JAIWoV0OrFuNh48veg9PIpJ7dEliuu82pDFjTMcClmOKDx9i/ZcfUZp5GAAPXz+G33grvSdMRq1x50wpaKzlcF0+FqcZcAclAF4aDX18Ywj2DrvoEvK5XBZqajbi5ZWM0RhzXuow15tY/s9/kJ++G4DeEyYz/q770OrO79CXIAjCiUTAInRIdfFR6ivKqSzMp9fYiRh9fC7YcNG5BCzgnrdzaOtGNnz5MebKCgC0wQEETh2PR68koowe9PBPxEvv21lNPu9Mpt3UNxxArfIgIuLG81aPIstsXfQNm7+dD4pCcFwC1z/y5CW5RF4QhEuTCFiEs6IoCuU5WZgbTCiyjKRS4RscRmDU+dvttz2Tbo8x2W0cqiuiwlyJorhajmslhXiDF+ZdOWxfvBBLg3vvociUXoydezfhSRc278m5aGjIQKXS4+mZcMHqzN+3h2Vvv4aloR6d0YNrf/9/JAwYfMHqFwThyiUCFqFTKIrCtkXfEJKQiCSpON3AybF/SB29Jr1mFyT0OnNbAE+1RE+fUMK9Y1Cp2l7lYzM3sX3Jt+z+cQlOhx2A7sNHM/rmOy6JngOrtRRT/V6CAseiVl+4FTwN1VX88OarlGRmIKlUXPfwEyQNHXHmGwVBEM6BCFiES0ZV1Vp8fQeg1XbukE19VSWb//cFBzesAUVBpdbQb/I1DLvh5ot6mTeAosgcPfoJoWHXo9cFXbB6XU4nK99/i0NpazF4enHXG+/j4et3weoXBOHK09737wu/BEIQfkGSNLhkS6eX6xMUzNT7H+H2V94itk9/ZJeT3cu/57+/v5ftS77FYbd1ep2dRZJURETMobp6Hbt234LT2XBB6lVrNEz57cOExCVibWpky8KvLki9giAIZyJ6WIQuV129nsDAsee9nvz03Wz44iMqm1P9ewcGM+rm2+kxalyHU/1fSIrioq5uB05nPT6+Ay5Ij0v2zm0see0FVGoNc/7yChHdU857nYIgXJnEkJBwyaiu3kBg4JgLUpcsu8hIW8fGbz6nsboKgOC4BMbedjexffpdkDacLUVRMNXvxmGvwd9/BBqN53mta+nrL5O1fTMGbx/m/PnlDmUUFgRBaC8RsAiXDFN9OlqNDx4eFy6BncNuY/ey79n+3QLsFnd+lrh+Axlz67wuT6R3JorioqZmMwouAvxHoVK1fwfqjrBbzCx48RnKsjMxevtww1PPE5rQ7bzUJQjClUsELMIlw+lspLomjdCQqy943eZ6E1sXfU36ymXILhdIEr3GTmTknLl4B1y4ya5nw+WyUlm1irDQ685bHZaGeha+9GfKc7MwePsw96U38A0JPW/1CYJw5REBi3DJaGrKRqUyYDRGdVkbastK2Dj/UzK3bQJAo9Mz8NoZDL7+hot6c8DGxiOoVAY8PNoernHYrFjq63H/mCu0/LQrCgoKxw64Pym0/Do44Xu7xcyC55/G6bCjM3pw7z8/wuDldX4fTBCEK4YIWIRLRl3dTry8ktFovLu6KZRkHmb9Fx9RcuQQAEYfX4bfeAt9Jk5FrTk/Qy9nw+Uy43DUIcs2qqrW4O3dB3//wVRVVWG1WgGwm5v48eU/YTc3dWrdvSdMZvJ9v+/UMgVBuHK19/374vkNLFyxXK4m1OqL4y/2iO4p3Pzcq2Tv2ELa/E+oLS1hzUfvs2f594y+ZR7dhgzvlH2HXC4bFksBVmsxLtmKy9mAj29/vDyTznCfFZNpF4riQm8IR63SExo6jT17sggLy0elUmGz2YiKisLh5YVGp2sJWFRqjTvoam6/+5N0/Hmk5n2WWr5v3nWp+XtLvQm9hydh3bqf8/MLgiB0lOhhEbrchVrW3FEup5P9P69g87fzsdSbAIhI7snYuXcR0b3HWZVpNudRWroQrS6QkOAp6PXhSJKEoig0mbOxWoubx2cUJEmNgtKyWSOASqXHy6snWm3rf+P79u1DkiQMBgMASUnuwKe2tJj/vfA0jdVV+IaEctOzL4k5KIIgXFTEkJBwyaip2YS//zAk6cJsttgRpqKDmEqOcGjrPo7s2IfT7k71nzR0BKNvuRP/8EhsTgs51XtxyRZCvGLxNYRRb6umIut7ErwicalklMYyGixZVOhriIi8hQD/YWi1/p3Wzj179tC/f/82z9VXVvC/55/CVFGGV0AgNz37EgERkZ1WtyAIwrkQAYtwyait3YaPT1/UakNXN+Ukpek/Et73WlwOCwU7f2LvzxvIO5DdnOpfTdLA3qiTfSA8jB5xfahoOkpJQz46lY7Qknx69b0FtQxq/3hU1iYo2ATdp4Kuc3OolJWV0djYSLdubS87bqypZsGLz1BTfBQPXz9ueuZFgmLiOrUNgiAIZ0MELMIlw2Tajadn0kUx6faX9q37DLXcqyUTriLLlOdtoCwnk9KcowDojB7QIwLVhETUWi2eWk/ssh3T0R+ZoEgkhI88oUQV+spC1N2vQxM/vlPb2tjYSEVFBQkJbe/ybK438e2Lz1BZkIfBy5sbn35B5FURBKHLiYBFuGTU1+/DYIhCpwvo6qacZP+BBfRIuR6NRo8sy3zz1md4eXiSmNyNxrpSjmxZQUV+DgAGb1/G3X43PUaPQ6VSoygKf1k9k+eu+q6lPEV2Yq/Lxlq7H4dvEIGB4zplEu8xBQUFBAQE4O3ddvBnbWxk4Ut/oiwnE53RyKwnnycy+ezm4wiCIHQGEbAIl4yGxsNoNb7odEGA3JwTREZRZEAGaPna/c9VPp5XBBmU498fu09WZPY0ODGqFWTFnU9EQUFRZPddx75vzjUit5RxrBR3evrysoP0bkrGqPVqngsr405fomCqMTF0+mgyNq1n49ef0VBVCUBwbDxjbruL4iALn2x/hsc9H8MnwJuQQe6JxQ5LAyqNFkVycPTgl0TG34ruhH+zFfm5NFRXoVarW9qj1miISe3brtfzwIED9OrV65SBkM1sZvGrz1F8+CAavZ6Zf/xTu8sWBEHobCJgES4ZLpcFk2k37iW2Ktzra1UtS2wlVMe/b7lG1XzuhGtRNb9Juz/+U2pjcoABD7X7HlXzeVVzmZLUfFxSNd/hLkeSaL5G4kihCW+jkfhAT3fZEkgqVcvXquahIqfdztpvPuPQqp9w2tx5UIzdIpHGdiNeSWRUn/40FeWCpEat1eGw1uPItqCKl6jJ30zK+D+hC3cv7XbYbez+cQnx/QcREpeAw2rl8OYNBMXEEt4t+YyvZ2NjI8XFxSQnn/pah83Kkr//lYJ9e1BrtUx/7Gni+w86h/+KgiAIZ0cELMIVT1YUvq+oY1yAN37as0s5lF9kwqKCHhG+7bp+44plOMqK2bviR2SXEySJ0OReXPfAIyctJ6767BCKw4Ut14QhJQCTtRKlnx61VktgVAw+QcGtrq+vrKA0O5PEQUPRaLWnbUdDQwMlJSXUrl6NR0TECYGcQlleHmHxCbhcTvZtWkNlcQEqtZppDz1O0tARHXl5BEEQzpkIWAQB99DNiqp6unnq6ebR8VVIpRWNlFns9I9t3/ya/LwsZJdMgJcPG7/+jCNb0gBQa3UMvOZ6hsy4Cb1H6xVCiqJQ8c4e1MFGTLoqAvsn4JfQ9rJj2eUiZ+c2tEYjsal9WyYDH9NUZ8PlkvHw0VFeUk3hkWzMWXtQnE4mPfTQSeW5nE6W//MfHNmShqRScfUDj9Jj1Lh2PasgCEJnEAGLIJzgUKOFfIuN8QE+GNWqM9/QrLbWwuGaRoYnBp/54mbb1q9l6Fj3CqDSrCMs/ec/aCgrAUBjMBI7ZAQRffrjaLAxIGkwjqONANhyq/Ec4kXV4XJcahfxN5w6mZ7DZiV75zYMnl4t+wGZ6xvJ3VNB36tGodYYMXrrMDXUUl5QhLPgAOl5eaRERTLx/vtblSXLLla+/w4H168GSeKqe39Hn4lT2v28giAI50IELILwC05ZYX1tAy5FYaS/F57qMyeqs9ocbM2pYlzP8HbXk3n4IEUFGcT3SiU+KgVFUcjZtZ20Lz+mpqQIAL+wcML6DyJVPZDo2X1p3HgQa2YRXsN7INssWK31FB3cRezUa/BPSGx33cVHCqk8WkK/ScNajh1Oz6G+tIy01d8xZ+oUPMLDCejVq9V9iizz88cfkL7yRwDGz/s1A66+vt31CoIgnC0RsAjCKThlhc11jVhkmVFnCFxkWebrnUdxumTuGB7f7jr2fvEjWT75zLr2PtRq9/wZ2eVi/5oVbF4wH7OpDgD/6BgS7UbUqVGM+NVDLZN4wT1UlLt4ITZzE8m33NZSzulkbj9ATUkdw2aMwm51UlLehJe3jvKCIsw1dehsleh0OixVVfSbPfuk+jZ8+TE7ly4CYNTNdzBkxk2duuxaEAThl0TAIghnICsKq6vriTXqSfY89fyWBdsLaHS6uGtE2wnZ2lKy+QBOH4kjdYe4atRNrc7ZLWZ2LF3Mzh8W4bTZAIjo0ZN4nxCGPvLYSQGCtbKSI4u+Jah3byJHjGo5Xp5XQu7ejJagI+NINYpDQR8QhHd8DCqtiqAgD8wNdhqb7JhriyjOWMeY7knYzE30vvZafCIiWtWlKApbvp3Plm+/AmDANdMZd/s9J82VEQRB6CwiYBGEdjrUaKHYamdSoE+bvQkr9pUwpU9EG3eemqIorPjkEyJG9KBP8rA2r2msqWbzt/PZ9/OKll2RExKiuOoPL+AVEHTS9cUb1lK4bgNKyhgie8Swc9k6bnj8npbz2Xl17NlfwZRJ8fh4tL2KKOtILhmr0/CPDSUqpTvx3doOwnb9uIR1n/0bgB6jxjHltw+h1px+ZZIgCMLZEAGLIHRAbVExO3bvw1Ptzu9yjAKUFzUyMDqq1fWSRoVXZCCeEUEY/NrOKpu+cxux3brh5xd42rrXbP2Y4pXpVBzMBkCjURHrZyA+JhXP0Ch3I1Cw5Odj7N+fcmcA5joLA6/pT0BEWKuy7DYn3yw6wm1zeqJStT2Uk13dwOe5lVxdV4BfaAgpfXq1eV1G2lp++tcbyLJMXN8BXP/oU2gNF99+T4IgXNpEwCIIHdCYthGv0aPaPJfz6cck3nlXq2Mum4O6vBJq9uXT7aYxbfbMyLLMjrT1LSuGACwuJzetfo5+Hi5GBEVBc8q7PuHjsWUVseGzf1Nd0QSAViMxfMYsBt5wBypV63k2dRW17F+7n+ie0cT1bj23ZuPuUiL8jCQk+J3Upu+zK8iuacLskLH6a/E6tJ9R772JOSiEph49mfmXp1tdn7d3F9//46847XbCuiUz64k/Y/QWP1+CIHQeEbAIQhtksxnF5TrpeNOmzfhMbXsp7+G33yRs6lQUWTmWe41jSfzN1SYaMoqIHNPH3TMjAZKCAkiSQu7BTGoTKrConLhsNcj1hZgbK6i1NzArOJWAwa+gSO4hJOvax/AY8VfW/fN5Mg/lYLO72+kfEcWQaTcSnZiIJEkoLhey04lJbaPwQCk1hbVE9EkBWUuTxcnP2fU0VjgYMjHQ3VgktlodRPsHMDzKn1FR/pgdLmoabIT5GvhiaSaSq46omgridBL60ECipo7AJbs4tOE7yrOOcHD9dpwOJ/4RUdz49PP4BIWcl/8+giBceUTAIlzRqt5/H210NGqf1hlqnRXl6BPbWCYsSRj7tr2fTtWunTgaGprnmQBKc4r+5vOWqgxQavAbMhFL1U5c1mqQVKBIKDI0yUcJ90xGbQxEG9wXXUAvJLWW2poiFi74mKghYwAo3ZnLtJQwkCRkl8ye3fs5snsHTrs71b+HWk1kRAi+3t4osgu5Xwo+PVNxulzkFVTg7efF1UOmtOxBdKKNqw+yI6OcxEFJoIBeo8JgUJO2uRil2sa1t6QwIMGdHK9692Fq9hyhPsVJ+c8/MfWZD6gtKebbl56lsboKr4BAbnz6BQKjYs7mP40gCEIrImARrmiKy4UlPR25sdHd86EoaMPD0cXHI2nOLk3/6ZjLsqj77AWC7/sLGu+4dq+qSV/zDX0nzAFg/vIsbp6ciOqExHaWxga2Lf4fe35aiux0AiClJNJ3xs1M7D+8VVmltaWs27eOZHsjoVpt81wcd5eQ7JIpya0hNiwYn0GTaDiwpfkuCYdLQdVYhWfyAHxTB7vrraim4OsVOFM9QCeROmo69VWVLHzpWWqKi9B7ejLrib8Q0V3s9CwIwrkRAYsgnEBRFJwlJdjy8lGcDgAMKSlow8LOcGf7NX79Nl43/75D9yz99iUKw4djczqxWVT08fDm2jFDTrrui0834Ve3iSObNwAgadR4DBrIvF8/ilZSoTIaqS0vJGvzMqQV8+k19iqMMx9DZWy9DYDLbqNuzyb8+o1ArW89gbZyzUJkz3BChgxHkiRkh4Oc/y6m1lCFX+9kug+ciKWhnsWvPkdp1hHUOh3XP/okCf0Hd/CVEgRBOE4ELIJwGoqiYDt8GEd5OZIkoQkNRZ+cfE5J0joasFhqSil9czIhT6zDyyOQ0qKtfLd9JQNUPYiLSiCoT2/UOh2NFjv/+fYwv70lldqCHNZ/8RFFh/YDoJEgRq8j9NoJ6L296TdlLmqVGsVmxvLVcxhv/jOSwaNd7Sn64HEiSj6kasx/CZk4q+X42y8vIkp3kNAbb2RkbA8cVitL33iZvL27kFQqpv72YXqOmdCxF0sQBKHZeQlYXn75ZRYtWsThw4cxGo2MGDGCV199tdU29uXl5Tz++OOsXLmSuro6xowZwzvvvENSUtIpy/3kk0+46667TjpusVgwtHMZpQhYhHPhKCvDduQIiqKg8vDAo18/JJ2uQ2U0fvMOXnMebPf12e/9miKLmdphs5BUKuoKNjEydDCSSkVeWRG1qm7oInpjcpnZ76qkyi7hUhQ8VGria4ox/rQIp9m9D5GHRkfkiKloQhJbEslJDiv+lt0MmPvHM+7uDFCfk0ndsn+h7zOFgGGT2ZBeRlWdhUazk3tmpPDN6nfxVdXi4ekNMuSuOEB1RikAY+fezaDrZp2hBkEQhJOdl4Bl6tSp3HzzzQwePBin08nTTz/N/v37OXToEJ6eniiKwogRI9BqtfzjH//Ax8eH119/nZ9++qnlmrZ88sknPPTQQxw5cqTV8bAOdNeLgEXoLK7GJizpe8HpBI0Gj379UHl60lBcTMn69ciKgt7Xj4Rp17a6r6MBi6OpjvKNnxM+4T7U2uPBkex0UGmqIdNkpdxkwlW/E29zA2PH3IaHRyBV1nrqrFXYD+zg8PJ1lBZVYbO5J+bqQ6OZcve9JPUbAIDN0siu5Z8z7Pp7UbVz7k5t+lY+3llG7zhfJk0Yd7zXSVHYu/w/dBt7K16eniiyzPovPmLXj98BMPj6Gxh96zyRyl8QhA65IENClZWVhISEsH79esaMGUNmZibJyckcOHCAXs2bq7lcLkJCQnj11Vf51a9+1WY5n3zyCQ8//DB1dXVn2xQRsAjnhWK3Y967l7zPPsOVmYVh7DiswUk4GyqIjzgh/4kESvEBPFL7oh8+BJWPX5vl1b79J7ShQS03VVuaiJ33RMv5+opa8vdmo9G6V/rIgMVcjEQpvUfNRO/betfonNy1OKxWag7WsnPpIhzHApe4FLyGTEbyCUBXWsdM/wL019yF1IGdqjMzD1NfVcKgEceHexSXgy1LPkTlFYKnToWsyOTvPUj21p0A9Bo3icm/fhBVOzaWFARBgPa/f5/TcgmTyQRAQIB7OaSteV+UE4dx1Go1Op2OjRs3njJgAWhsbCQ2NhaXy0W/fv144YUX6N+//ymvt9lsLfWB+4EFobNJOh2eQ4aQOsQ9Ebbqx92YV65HUil4XDcMR1kTulgfdNHeSKqrceZk4aooP2XAovHzwmvO8XkuB156i6Z1e0GRsdns2K02Bl8/utWmhDAAl92KKfsAdUcOIp2QwdYbFVXFtYy46Vb6XnU1mxd8yf41K7HlH8ZemEmP0ROISxmBbtAs7D99hu7qO9q9gql79xSykdm2eR1DR4xzvx5qLQOn3ccryzO4e1ACUf4e9B1/EwfWrmLlh+9wcN1qLA31THvoj2j1IiuuIAid56x7WBRFYfr06dTW1pKWlgaAw+EgKSmJIUOG8MEHH+Dp6cnrr7/Ok08+yeTJk1mxYkWbZW3dupXs7Gx69+5NfX09b731FsuWLSM9Pf2Uc1/+8pe/8Nxzz510XPSwCOeTIivHcrG5c7FIErLZga2wAVxOlC2foEsKQxMR0ZxITnXCZxWO7L04dQkYx1yDOaeEop8OYhzQC0UG2enEaTG7VzQ12IkI9Dv98Erzj27pkXJqk4wAOBoc6KVKjmxdRUX+YQAkjYaek6Yw/prpFC/9kvi7H+vQZoZHczJ4c/F6fA0qUCDUR481cjjZlY28c8uAlutydm1j6Ruv4nLYiUjuwcw//hmDl1eHX2NBEK4s531I6IEHHuDHH39k48aNREUd32dl165d3HPPPaSnp6NWq5k0aVLLX4vLli1rV9myLDNgwADGjBnD22+/3eY1bfWwREdHi4BFuHgoCihy88fxr+vXpmHs3ZuijYdRBwfhFepJbX4BTbVm/ML8QC2hFGUTNW0a6jPsQyTbXaT/Kx3vnu7raosaGTivByqViqOH9rP8w7dpKHVPjPX0D6DX4BE0FR5m6nNvtvsxaqvKycvLYcDgES3HvthawCeb8hmXHMwz03q2HC86fJDFrz6P3dyEf0QU1z/yBEExce2uSxCEK895DVgefPBBvvvuOzZs2EB8fHyb15hMJux2O8HBwQwdOpRBgwbx7rvvtruOe++9l6KiIpYvX96u68UcFuFSITc14SgtJf+Ld9AOvgav0EC8woPxCA0DjR4Ax/5toNWjCgoH2YXKLxBJpz+pLHtJI5aD1fheFesuW5ZBVpAkFZJaQpFljmzdyMavPsVUUQ6Ah5cPIYE+zHr1vXZNkDVVV7Bx62b69O5HdEwc3+wopKDazKeb87lhYBTPT09tdX1lYT4LX/ozTbXVqLU6Jtx1H70nTBaTcQVBaNN5CVgUReHBBx9k8eLFrFu37rRLlY/JysoiJSWF5cuXM3ny5HbXM2TIEHr37s1HH33UrntEwCJcckzFUHWkebhIDWX7IGEcuOwc3rMNuaoUq82CQa/HUVWAVVIhR07F4O8OTvQ6AzpFwtigxmdCDCBhWpaHJtCAy2TDbDyIJlSFw1aD02Yna0MdOQd3ojTvhOQf5Mc1j/yJsG7dW5pkKduG01qOIjtBdlBXugpP31Ts5mIOVzRQa5UosI7hkTtuOe2jmetNLH/3dfL37gIgZeRYrrr3AXTG9uWEEQThynFeApb777+f+fPns2TJkla5V3x9fTEa3WPoCxYsIDg4mJiYGPbv389DDz3EwIEDWbhwYcv1d9xxB5GRkbz88ssAPPfccwwbNoykpCTq6+t5++23+fzzz9m0aRNDhpyc9fNcHlgQLlpOG5hrwFREreSLXetLaGgoTruNFR+8iU6jJbuijhCdltycTMZPuoaa7Wn4zrkJyaBFQcGeX482wgtFUnBUFqONb0LS6XFVO0hfl4F3pRO9txfFGQeQmzeBTB42jNG33YtvSCil2/6Iy2UmOOkeVHo/1MZQVFoPsg4vpq7uMCXmRHJL9Qzp3RNHrRpJL+HSWpEVhWqtHo3egwabix6BnuyrMNG4ZRWutKVIioxvWDjXPfwEofFt7OUkCMIV67ysEnrvvfcAGDduXKvjH3/8MfPmzQOgtLSURx99lPLycsLDw7njjjt49tlnW11fWFjYahVEXV0dv/71rykrK8PX15f+/fuzYcOGdgcrgnBZ0OjBJxx8wjFarTRWVroP6/TERCfTe8YMrvrFLT827Gfw4LHHf57a3r8REiAy4ghfffAixtpabvzzy+xfuYyMjes4snUr2Tt30m/qdQyb+Se0Oqjc9yp+kdeg9XEP+QaHDaKodBMjet/AqIERNFhs7NmfjlqtYujkAahUEov2ZfBDcTl6vZHoQA9u7BWJ/4B7KZ40ih/eehVTWSnzn3mMcXf8in6TrxVDRIIgdIhIzS8IFyFFUcjOziYpKYmGhgZyVq2i36yTM8nWV5VycPU3DL/54TOWl5m/j0Wfv4FcUM2g4VMYfM0MHFYrqz96m7KsXAB0eok+oxIYcccrNOTPR6P1wSfxJvbuXQBY0Gj0HPuV4eEZSlzsaNTq43/3LN6xm24xMfRuyTXjZmlsYMV7b5KzcxsASUNHMPm+32PwFKuIBOFKJ/YSEoRL3JIlS0hISCAoKAjPrCx8xoxp87q8zB0c3foz+oBgcLlQnE6aPvoMr7vvILO6hIqacpIGjiAmqjsNjXWMHnwtiqKw8evPSR4+iuDYeHav+IKtSz7FWuPOnWLwdDDhrseJiXHRVLODkP5PAeBwWFGpNEiSirq6QvLyVmE0BlNQ8DlRUTehKAoVOJC9gxgUPpIAg29LOxVFYc/y71n/xcfILic+wSFMe/hxwrslt/lcgiBcGUTAIgiXid++t5abg52MvfGXA0LHyVYriiSBRg2ShFqlxm4zU5KdTuXazQy8/5FfJKNzW/PxB4yYfRsGTy8KjvxE+sqXKDkQS1NdLQChCYn0HxlAQJiT8EEvtFl3Y2MlHh6Brcp3uGzsKNlCo6MRg1rPyOjxqFXunpiynCyWvvkq9RVlqNQaxtw2jwHXTBdDRIJwhRIBiyBcRsy7dvFO1lFGJcQwcsiAVufm//gqwRp3T4ZWZyA8vDtJ3Ye1BBDF69fiNOixhnpSXV1GTFR3IoJjsTU1suXbrxh/570tieTyD36PqegbqnMC2b+lAKfdPTE3vt8Axt7+KwKjYjrc9hprAzuK1xLkEcLA8GEA2MxNrHz/bTK3bQIgacgIrn7gUbTt3OwUQJFlHDYrDpsNu9WCw2rFYbXitNsJSUjE6OXd4bYKgnDhiYBFEC4zzqoq6hYtxpCSjD4xEW1kJACvfXgnf7j3EyRJwtbUQGH+Po7kbUevNaLIMi67HaeixSc8mu4J/cgrOszW/T+jscMD815ErWm9k3PuwSVodN4EBvdny8Kv2bd6OYosI0kqUidcxYibbsPLP6DD7T9YnUWp6RAT4q5DpVKhKArpq5az9tMPkZ1OgmPjSRw0FIfVgsPaHITYbO7vbe5gxGGzYrdacVgtOO32U9al9/Ti+kefIia1T4fbKQjChSUCFkG4TCmKgj07m6LMTBptNkxN1eQ4yunbc7z7fHNw0WvAQPR6AxV1pew+solGaz1IErLiom/8UJJjep+yjux9i9F7+hKdOIGakiLS5n9C9o6tAGj1BgZdN5NB181CZzCesgyXy0GFuYKSxqOYrLUogNXRSFZJLqEek5Ak9y4H9uJCqpd8jWJuOrsXRJLQ6g3oDAa0BgNOu53GmmoklYrxd95LvynTxHCTIFzERMAiCJexsrIy9u7dS1BQENGRkRw6tJ/inFJ8PLzwUuuI9ggg3ZbBzyW56PAiNWYAjWaw2F1EBnrTr3sUfXsknLaOnP1LUWvUxPW4BoCijANs+OJjSrOPAKDz9iL8qmH4DUxCUquQgFa/TCQ1fsYQIrwiCTEGoj42RFVVxY7iUmb0PR4w1VdVsHv5UlwOO1qDEa1ej85gRKt3ByHHPuuaz2kNRvdxgwGNVtcqIHHYbaz68J9kpK0FoPeEyUy857cn9SQJgnBxEAGLIFzm0tPTaWpqIjk5GUmS8Pb2RqvVUldZRWluFq9sqePZGwYQHuRHcWkl3RPce35lF5TwzGfrmNQzHG8PPfVmK74ex+eOnPgLobIyncEJkfQcOplKcxXV1gZ2r/43zk2VWKvdE3MDIqIYfdtdJA4c0u6ejK927GJQdBRJYaGd9nqcSFEUdv6wmLQvP0ZRFCKTe3L9Y0/h4et3XuoTBOHsiYBFEK5wFruLvUfrsDpdnBhGBHjqCPMxEOilR606fYAxf3kWE1JdbM/+NzE9biXUwx9/vQ9atS/pq5azZeHXWBvqAYjqkcrYuXe3SvV/OqsOZlBoqmdqSnciA/zP9jFPK2/PTn5462/YLWa8A4OY/n/Piky7gnCREQGLIAgnebegnEiVhm/yK7kpwJcii3viqgwke+rJaLSikSRMmTUMC/NFqXWQ4GWgtugw+yUTc+bOxFN3fGjF2tTI9iXfsnvZElwOBwDJw0cz6pY78QsNO2N7ZFnm0607GJeUQHxw8Hl55pqSIr772/PUlpag0emYev8jJA8ffV7qEgSh40TAIgjCSRaX16KWJFyKgpdaxVVB7uXQTllhXW0DnmoVw/282JxZQa3ZgV4tMTQxEE+dmppFaazydjF1zAj8PVtvYlhfVcGmb77gUNpaUBRUag39p17L0Fk3t2t58be79xJoNDK+x/lJImdtauTHt19r2Yxx6Mw5jJx9W8tybkEQuo4IWARBOImiKGSZbdQ5nOxvtDA3IhD9ad60LXYn/9t1lDuHx1O7MA3f64excPUGxgwdQGgbwzgV+bls+PJjCvbtAUDv6cnQGbPpP/U6NDrdadu2JSuHSrOZ6/ueevXSuZBlF2nzP2Xn0kUAJA4axjW/e1TsIC0IXay979/izwtBuIJIkkSih54fK03sbTBTanOc9nqjTkOop775OwWVVsuNUyewdVc6hcWlJ10fEpfAjU+/wA1PPkdQTBy2piY2fPkxHz/6GzLS1qLI8inrGp6USLSvLwt27T2HJzw1lUrN2Ll3c/UDj6LWaMjZuZX5z/yBurKTn0MQhIuP6GERhCuUXZbZXNeITVZI8TQQa9SfdI3F5uSzHQXcPSyexiWb8L/h+NyP5es2kRgTRfeE2DbLl2UXhzasZdM3n9NYUw1ASHwiY+feTUzqqbaVhkNFxewtKePWIQPP8QlPrTT7CN+99iLmuloMnl5Me+QJYnv3O2/1CYJwamJISBCEdjvUaOGo1Y6vRs0QX09UksRP6cVk1pn59bB4DHoNtQvTWgUsAGu27MBidyA1r0NSUHil3MRjwd7oVGpAQXY4qdm3nerdm5Ed7km+njGJhAyfgD4wGIenFzMGtc5Im1NeQVpuPvOGDzlvz9xYU82Sv/+VspxMJJWKcXfcS/+pIsmcIFxoImARBKHD6hxOdpiacNXbsGTV0bd3GAkR7p+n2kVp+M0cddZv6OZ6E1u+/Yp9q5cju1xIkgpdUi+uv/0uYrp3x9RoY1d+DTaXTL9ofxyKlRWHj3DP8KFtbtzYGZx2O6s+fMc9WRiRZE4QuoIIWARBOCeyxYm9qAHFpYAEtsxMPIf3RBt0bj9fNSXFbPz6U7K2bQZAo9MxaNpMelw1naUZVdw5MoG0jHJMVgd1DiuWQ0eYd9NEtJ7t3xixIxRFYdcPi9nQnGQuIrkn1z/6JJ5+5yc3jCAIrYmARRCETqPICk3bDiGZj2IcMhTJ0/eclwQXH8lg7UfvU56fA4Da6Elenwk8fMsc4sP9Wq5zOZ3s//pHes2agtbj/AQtAHl7d/HDm69it5jxCghkxv89S2hCt/NWnyAIbiJgEQShUyn15djXfY5T9sWRl4kmzL1btMo3AOP4G5CMnh0qz2lqpH7tdvKzD7Lr4Bbqze6Muf4RUYy+9U66DRrWMvzkcjjZ8+ki+t56facFLYqiYG0001jbQEONiaa6BqqLCjm0bjGWhlo0Oh1TfvswKSPGdEp9giC0TQQsgiB0LlmG3LXQbWKrw9YtP1H52ot49E7Be+bt6PqMApW6zSLqV23DUVEFgNrTiNe4wWj8vHE5nez7+Se2LJiPpTnVf2RKT8bcdjcR3VMAcNkdfPen1zBGxhMUE44su3A5XWh1Oo7vgCShwAlTgN3Hfjnr5tjVeqMBDz9vvPy88QrwwdPPG4fNwo9v/Y389N0ADJ05m5Gz54okc4JwnoiARRCEzpe9GrpNavOUYrdhWbcEy+ZVSBLITWY8J0/HMHYmDWu34yippHHjFkL+77cYk+PaLMNmNrPj+2/Z9cN3OJtXFHUfNorRt9yJX1g4ssNF3pIc4md0Q6VxBxCKonTayh6H00VZrZWS6iYyfvia6h0rAUgYMJirf/cYBk+vTqlHEITjRMAiCELnO03A0hbn0SysG5dhyajEe8wgpLh+NBwtJ2DMYFTqU/dYNFRXsembLzi44eeWVP99J1/NsFk3o8ZA8Y95RF0dh87v9MNDLpdMQ72N0lorFXVW6u1OlOOdMS1dLZIEigIqtYS/r54wPwPh/kbyt6Wx4oO3kR0OfMPCmf7Y0wTHxLX7+QVBODMRsAiC0Pk6GLCcSLE14dy7gU0bPkSOScUzfBxqzfF0/YpLRutpoN+AoS3HKgvy2PDlxy3DMzqjB0NnzqbP2KlUvbKH3dNjkSSJhiYHRoPGvfv0CZ0tkkrC4KUl2NdAmL+BYKMOdQd7Y8pysvj+9ZdoqKpErdMx5b7f02PUuLN6DQRBOJkIWARB6HynCFjsTRayl63F0+94AGJvtFKVX8XwR+a1utZha+TQur/gdDShVGTgP+AuKqrC0Rk96Nm/P0aPkyfv5u/bw4YvP6YyPxcAL19/+s+6DbOxO6NGxaE5TW9NZzDXm1j2zt9b9kjqP/U6xt5+t8jXIgidoL3v35oL2CZBEC5TGr0W7wAD0RMnAFC6eQtN1WZ6Xjf8pGu1ei/6Tvk7ALLspGbvT5Tvf4zA6E9xaepx9dGhNrYOBEJkH67yH83RkB7sydhEo6mWtI//ib9vCHUHJjDjgbmd9ixNTgf768o4YCrH6nIC7k4b+YapqP09cK3fxJ6fllKem811jzyBV0Bgp9UtCMKpiR4WQRDazbH/K+xqF6gkFEkNkgpUKhRJIm9jOeG9BuFyuqgtKKfHnBmnKWcbclkBkkaLFBCBtq97GMhRY6Fi2SHsTiuEqFGp1AT0iEVdVUPNt7sJefAqpCAf9ixfyrbF/8NuMQMQ26c/Y+feTXBs/Bmfwex0cbihmgOmcurtjS3HW1YOqSR6egfR1z8Sb53xpPuzd25j2T//jsNiwejry/UPP0lUz9T2v4iCILQihoQEQeh0FZUr8Pbs6Z4mIrtAcSEpCsgy+fP/jF9DFD63/AZdcARq48lv9sfYN/4ABh90g07OcZLz/Sa8YoIJ7dcdp91O5b5sbD9l4AyqRtGr8AoOIWT8RGwuJ1u++YJ9a1Yiyy6QJMKGjcQydgQWT3cPjc3lRK/WtExrUQCdpJDo6U0fv0iCjf5ntcKotrSY719/iarCAiSVirFz72bANdPFPkSCcBZEwCIIQqerrl5PYODYNs+t/WAq+hKF3r95G+/wZPeym1O8gTuP5uFc8T6GX70KQEOjjYOHK1EAFSDnVdKUpGNSv16t7lMUhbqio1Sv+dkdKBkMmC1m9u/ZTml5CQBqnY5B185g8PU3ovfwOOdndsku9tcWsasmD4vTfjz4sTuQf1iHY18GAN2Hj2bKb36PznDqQE0QhJOJgEUQhE5XXZ1GQMBIJOnkSa47199PgyqWoEZ/zI0WjAYdiaPn4OkX0Oo6xSVjX/M/VMERaPu5e1hW/XMH/W9NxctTxXPb9pLq74u5IJeeWql5qOaEdHCKzPBJk9H8YsJradYRFr/3KpbiCgCM3j4Mv/EW+ky6GrXmzNP1Sptq2VyVSZnZ1Oq4JEkk+UQwJCgRX13rZdSKorB3xQ+s/ew/KC4XAZHRTP/D0wRERJ2xPkEQ3ETAIgjCWamqXoeiuHA6G9CovZBUGpqasomOup2Ghgw8POLRan1Pum/d90/j6QgnZdQ0ZKedon1pHMw4jN2pMOPO3+EZFINj03IUaz3aYVNQ+QRQUFpPSaEJu91Fj17BhAR48vm+DG7v0+OU7cs+eICNa1Yz78GHTzqnKAppP/2PXYsXIpvc81v8wyMYdcudJA0ZgcVlY2dVLofqinH94lefv96XYSHdiPcM6PDQTvGRDJa+/jJNdTVoDUaufuARkoaM6FAZgnClEgGLIAgtFMVFcck3GA0n/uV/vNdCVhxYbA1o1J5oVAqNTUeQON6L4nSa8PMbjKw4CPAfjlZ78k7GRVteQOszjgNbVhEanUDqlHngcuI8vBFXeRXrD5YgRw8nPc/BTX6H0ZV9SGaf+xh/zbxWAcJ/9xxgTEQoWXvmoZJ86Z36LOHhyahOSI2/Z3MadqudoRMmYs7by/cf/IOsWi3ORgu3/+VFtIqN/85/A5+CWiSzFQApOhzj1HH07zeS/v7R6NVtbx9wtprqavnhzVcpyjgAwODpNzJqzu2oOrkeQbjciIBFEIRWyiuW4+nZDZu1lONrYtwkScuCvRDj78DudKAo4GXwJjUqlEDvADQab5YcfAun4uL6Hg+iOyHh2zFVGx4mQBWD5LAhqTRQnYUjPxdnxHQcodPJKqzDXGMiTtuAp60Sn3lTyK7MxOmwktp7MmlH06hxqTA7ZI40ylwdEoBfYxFGowdl5QdISrgOe2k95ZmZlBUXU+nrg9VUR726iipzDiY/M7r6/owL1aJWxyIZjGyyyvQ3lVK8cR1Ouw2ApCEjGH3rnfiHR3b6a+xyOkmb/wm7fvwOgOhefZj20B/x8PXr9LoE4XIhAhZBEFqpr9+HVhuIwRCGJJ38V//6zErGdg9u+d7UVM3BojLqzCbKvllFXaAJeVgforX+eGi8W66zmK1UZ+0j3FiELJnQ2fMxloVQXqTh+oQwpNgRqK25SHIT3xWF8qE5kDuG+dAnxIWiOEnqORGzZOSvm9K5yr8BtcrANQNGtmpbaekRdi98EuPhWgylgSS/9FcCk5NbzjudTopycygpKGD/nl1Mu2k2wRGROJqsbCwqprqmksDD6Rxc9zOKIqNSq+kz6WqG33gLHj4nD2+diSzLZJkKqLaakBV3+CejUGc3owCN6RlUfPsjit2BV0Ags558TqT0F4RTEAGLIAgdsiO/Bn8PHd1COr7BX3l1PeW1DSC7KMs/wOTJ12C3Wvnzm/9iysBkTBYbkiRhtdnJKjfRM7UnmboIDFoVEhLljTZGRvtzTWIwCzf/SLSmHo3GPcHV4XKSYc8hNMCX/MKV1CvdaKiWmBIwtmUHZZUkERYVTVBgCA05ZdhrmrBkV+OVGk7pgQqqw2UyfAKYFhvAwe/nk7dnJwA6o5Eh029iwDXXo9Wfel+iSmsjuyqPYHFaWhY+JXqFEuwRhIR7YE0lgb/eB3XzTtXVRYUs+cdfqS0pxuDlxY3P/JXQ+MQOv7aCcLkTAYsgCB2yMauK+GBPIv3OblmupbGB2tpKXpq/gkhvDY/feyfrN64jt7iCQb26Iykyb6w4yEt3TiU84vhwTNq+bFyKhMNp5+VV+7E6ZO5POMwtNz+NWq3ltSWvUV9Vz9C+Q0mOTCYpPIknP7qXvvGD+T57GbfqZpAa3BMAvbcHfslRGIJ9aShupPC7HLwTfYm6KhZJBd+mlxLsoSPRUcL6Lz6iIi8HAK+AQEbOuZ2eY8ZTa2vgQG0+NTZ3UjlJAj+NlkEhKXjp2tcbY7VbyC07THFpDtlf/4i5pBKdhyc3Pf0CYd26n9XrKwiXKxGwCILQIbsKajFZ7IztHuLeRLCDNm9cw8HcIkL8vdFr1Qzs049te3bRt1cqQUFhGL28UZwOLBYzHt7H3/hfX7SJ4SlRRAb5oFZJaFQuPHUyXl7Hh6dcLhdOl5Mv13/J3HFzWblrLfWH8pC37eTnlDzeCPoVnjNvoOJgNU6TDXBvv6xSSURfFdtSTtm2DLZnVFLhp+L2qYPJ3bGZnz7/ENnUAIAuPISY66YwfPgkQjxOn3JfURQq646SW34Ei83c6pxabSA6OImYoDicNiuLXv4zJZkZaI0e3PDkc0Qmn3oVlCBcaUTAIghCu320MY/EEC/sTpmUMG+iA84t4ZrDbmf7zh34ehlJ7TPgtNfKssz7y3YQFejF4m++5IevP6W+tprU1F68+eabjB49uuXazJJM0jLSaKhpJGZ9EbE5xRhG34JfkMSu4kBqrS56jY1smVLsaLBSV1iFh85JWLBC3cEcwoIa8Q5UWGrRMXXOjfw+fQO/rbJx8IcfsJmbAHeq/zG33UVIXAKNFhP55YepNJWd0Gp3QOftGUG3iBT8PNoeRnPJLvaUFLAzNwfzdwtRiovQGAzc8PhfRDp/QWgmAhZBuILtzK/BbHe1Wgsk8cu1QceF+xroHup9irPnn8Pp4tPPv+D+++7lX//6F7JvJAvmf8bmVUvJOHSImJiYk+45si+PBlMTg0an4nLJqNUqSrJqKVi4loB4d++IztuD9J/fIWzkMBxZK/FL6kPqlEfBwxcUhR+2v0i0f3f6JM3G1tTItsXfsOenH5GbNz0M6J1E9ITRJCUNISogAvUpdoV2yS72lx9lZ2EhNper5bgKieTQUIbHJKKRZb577UUK9+9Fo9Mx449/IrZ3v85/MQXhEiMCFkG4AtU22VmdUY6HTsO1fcK7ujkdMnToUAYMGMB7770HuIeB4hKT6D9iHD2m/Yq+4a17fRQFSstMVNdqqbS4+E1yGMF6HVJlBX5hEj4TBwFQXZrPng/fxH/KTVRWZdJD8SB64nRUHu5Jtuajq2ms2oZP9LUYgvqxOu1zcldtpuHIUQDUWi0DrpnO0Bk3oTUaOVRxlG0F+VidcktbJCSSgkMYHpuIl05/ymd02G18/4+XyN+7C5VGw9X3P0LKyLa3OhCEK4UIWAThCmS2OzlQXM+Q+IAzX3wRsdvteHh4sGDBAmbOnAmAwyXz6MMPs29fOjOf/TcTkkPoE+3X6r5X3t3G2CFRDB/cOqeKOT0ba0YRKC6oLULS12OIDUOtVqPILjSN+1GnjIDkqS331GV+QVFVAUecnug0agaFTGbB+2/iOOoOXDAY0AwZTtLYSYzqloyP4dSrik7H6XDw41uvkr1jKwDDbriZETfe2rLiSRCuNO19/z7zBhuCIFwyjFo1FofrzBdeZN5eMx+Xy8WmqmxKt/2AAmgkFfmOGjLysrnZO4u3t2zlGlMys1KHttz3xAND+fGnLLZsOMBAjxpoqAbcv9i8QgGjEc3ECaiCo36xEeNNcGAhrHkJxj1OXW0N2ZkWnLkZjJ79CiFh7ozAR8dOonRPIRNrj1BTUoRzw1pKjxym9JY78R428qx2Z9ZotVz36JOkzf+UnUsXsXXh19QUHWXqA4+cdmm1IFzpRMAiCJcRSZIor7dypKwBWVGQFQVFoflrmr9v/lp2f1ZOONc/xg9vg/bMFXUys809Z+SG1FEMHzq85XjVqj1keezkt0OvhaGwLnc/f0v7H55a4/FgwR+qanOot5fTM3ESUbHj2tVb4YqfSGWtneKXpuEIS6bXjc/iPe3eVte8dt0NLIpL53+7A/ndRG/2f7+QuvJSfnjzFcK7JTPm9ruJSul1ihpOTaVSM3bu3QRGxbDqw3fI3LYJU2U50//vGbwDgjpcniBcCcSQkCBcZioarMiyO5GZJEmoJHdiNZUkIamOfe3+LDV/VksSDTYn645UMCDG/5xXCXWU3W7H6GHkv599xrxbb2s5/tBDD7F3717Wr19/xjIURSGvaDNFJTtxumwk6pKhshpZdvc4uRw21NpjPRgKKr0n/t36Umz14FBGJjOnX4UkSRwtb+JIZjVWixOtRsXk8bG4ZJnX01YzITYex66d7Fi6CKfNneq/2+BhjL513lnv0Fx0+CBL/v4i1oYGPPz8mfl/z4pcLcIVRcxhEQShQxRFoajWwu7CWqb0CkOjktCcYlXM+TBkyBCUcG/WLViGZ/PE1Z49ezJ9+nRefvnlDpUlO5189u3z3D7rGdS6k/c9kmWZqqPlFB3KxWG309jYSJXijUdwNCEBRnokB+Ljo6essol1648yYmwIN332HbIi85vRKdyUmMLWRV9xYM0qFEVGUqnoM3GqO9W/rx/33Xcf3377LbW1tfj6+jJv3jzefPPNU7bXVFHG4lefp7qoELVWy9UPPEry8NGnvP6X/vKXv/Ddd9+xd+/eDr1OgnAxEAGLIAhnpaLeSl5VE9VNdq7pfeFWGn3zzTfMnXs7N17zANPvmsy29Sv597//zcGDBwkJ9mbzng9AUjFxxOPtKu+rdR/SP3YIKfH9ALBbLXz2x2eI6T0Un+AgAsJDiOmViMHLndm3ocHGwZxaYqN9kBTYl76HcA87TllhX4ETPx8tHsYYjmRkI1VXcu8TN9FQWc6GLz8md/cOALQGI66oRB5/7XXWrVtHQkICKpUKo9GIt/fpl43bzGZ+fPtvLdsGDJlxEyNnz23Xbs8iYBEuZWLSrSAIZyXEx0CIj4F1RyouaL2R4YHMGvZbVq75im9/eJfQOB9uerQHn2/9LU7ZwbUpN+PjGdrmvWaHhZ3VuWSYSnDKzX+Dhcbxxt6NPPzzdso1sH/dagze3hxav4CoafdTYDKwd3NJSxkeBg09E/zYsKuQTbklGL1qmT0imYEJqfQfCbVHD9NYvhPPZD3+a4PIOZpJVGgMMx//M0cP7mP9Fx9RnpvNxuU/4K3X4W1tJCQkGJXq1AGH2WFj29FMDlcWoqAgjx2ARuXAuSud7d8toCjjANf+/o/4BAWfsgxBuFKIHhZBENq09nAFquYU/U0253nrbbGaG1mzdiXLDpSj0+gwVBiZNS2RfsP6smv/Z8iKTGVtNtdP+jvVlmp2lu2ksKEQWXHnQcloslBmUwj3DCXKw48kn1CmRfVHpVLx7ttvMqVXHxzpuzE1mDh6tJSamDj847sz+/bZrdrxxg+f0C0sGqNOT2p0N8L8w1rO2e1N1NRkU19fgKl0C6HGoWTtspFcEEHGsEomTZ8JisKsadfw3fIVLfcF+njTPSWFfkOGMPP3vyJt9xZeufNhbn3qQQZPGYdOpaF2by5/evBRduzYQe/evTGZTNx7x1yWr1yFw+kiJiiA11//B9NumdtS7iuvvMIbb7yB2Wxm9uzZBAcH89NPP4keFuGSJIaEBEHoNNvzapAkGBzX/vwuS39YjI+XJwA1dfXMnHEjALYmE3v27MRid1JTV0eAry97c0u4Y/rV1NfX8fSX6xkQpuGxe+9uWe3zn2X34dAa8I0aSt/gviQHJKNRtd1BvL+2mCVHvmREaG/WluZSl2PhEVcIHmWlKHo9+RUNMHgYEfGRxPTrgSRJ1FmaePzHT4g2+vPk1bNR/6Ls9PSvcLmsJCXN4OjRDVibirDXF6JzTseyvowmLwuTnriFospyHn/y/1j+zSIemjgClSTx+a4DxI8ext9ff4PeoTG8//77PPXUU6Snp6PVaunduzfPPvssDz/8MIqiMHr0aAICAnjkgfvZtegrfliXxo78Iua//jem3fcACxct5vbbb+fdd99l9OjRfP7557z99tskJCSIgEW4JImARRAuAyWNJRTUF6Cg4P6/+39S8142xz6finLKZPxtk5BOec+q/WZCfbSoVGq6B4WS4OmNtbqU5J79oI1lxGvXrsDuhIKKWtZmVtM9UEtoSAgmUx39YoOx2aykxEezbe8+AvwD8W4ObqpMjYwZOoSQ8OOrbqwNpazf+2/6ptxEWPDpNw5UFIWfDn/A1T1+A0DxqvfxiuyNR0Ai2jB3r4m5ppaCrKNUZmRRLJWzJ0xDrimbJ0bcSn7NKgpNOdg0kagkiaPFhcyNGYRWpTBw4H0AuFwO1Orjy78/WfZfKn/aSlNjBXv2ZJOWmcfT0yYQmtCNd3/exNARI1tNup02bRr19fXodDpUKhUrVqxAkiTWrFnDzJkzqaioQK/X43I6SJv/CTc+8DDjkxO5fuI43vxpPYOHD+f9999vKW/YsGFYrVYRsAiXJDGHRRAuA9l12YyMcCcoawlSziJZWWcYGekOBpyyk5+zj7B013+5PvVmfli2hIF9+9LU2EhpRTl1DU2sOVKFr05Gr5KJ9Tfw19sn4G/UkJmXjznEPcnV7lCRl5vF1NHDCY0/fS4Tg3c4U0Y9y+69H1FUsp1Bfe9sOZdRtpa82gxoCd4UfA0hLeeNgTGofD1bghUAjwB/egz1p8fQPpTmHyRi7RKGzHkGh1ZLocuGVtuH77MWc2vKHIbG3MDWYhvzeiS33H8sWHHYbWRsWIuyeieq8lJaptVqNcz5yytEpvTiP+PHn/Q8H330Ed27d0elUnHgwIGW/6a7du2isbGRwMDWO0VbLFbqbA4q8nPJyDjE4JhwcnZtI2HAECRJYvjw4axdu/a0r6EgXOpEwCIIF5Aiy9RXVVJXVureYK/5jaqxtpqeoyeg1rT+kVRJKtSnmbR5oUmShFatZWpyKptM64iJ60Z0bCIH9+3Ezy+A4cPHUpifhbmhjqun3ciOHVtQKU4Kjh7FFBRG6oDReHqcZTZXSWJA/3uoKNvHirQXGNLrVvwDEpEkDS5ZBhS6BfSmR/i4Vrf59ZlEyZb/Unv4ZwJTrsUzMgVoXtpckU/Bwa1ovbw5vPpb3va3MSlyAA/2mMKDPaa0lDE6TObD9ENMjY4gPigARVHYuXQR279fiLWhHgCd0Uh0YiRZTguaojy2Zq9gTEDrwOOY9PR0mpqaUKlUlJWVERER0dKm8PBw1q1bd9I9WgnyNq2D71Ziqijju7+9QHBsPMNmzeEy6SgXhNMSAYsgXACKLJP29WdE9+yNT1AwUT17odYcH1Jw2u3k7d1Ft0HutPM2l439lfvJM+UxKnJUVzX79Jo7NCRJIrXv4JbDLllBbfTh4N6tjBs1GrWuc9PNh4T1YXJIKnsPzGd/5hLUKh3+kvsNe0vF93y+6116Bg/GO3Tk8aZG90GNQmP+Mkx7P6a3LQ4J8AmOoM+I6/Hwd6/Ced9p4+9bP+aVo9l08+6NTqPm+j69UKtU/LZ/Kv/ee4h7gwKwW8xs+PJjADRaHaNuuYPU8ZNpLD5EeuNneOzK4cZpj3J4yw801de0an9NTQ3z5s3j6aefpqysjNtuu43du3djNBoZMGAAZWVlaDQa4uLiTnr22MRu9Hn331h9PNEaDFQW5LH0jVf4IW0nGk8vZJerXcugBeFSJAIWQThPXE4nRYcO4HI5QIFhM2ejM7adQVaj0+Hl58/8Ve8R1aMXOrWO3kG9GRQ26AK3+twlpaSSlJLa6eW++9l8ausbaXLIJAXqueHaq/HxD6Eg9wiRsd3QaDTUrPyJjIYiuveczODYmJahFllRcCngjB7BvtqjlFrNTI9MIa82j+V5G3BmOUEBjVrDrd2nEB8SD8DC3ftatcGgklAUBb2HJyNnz2XT/77A6bDTUF2JzmgkMGkQdvO7uOxWFFkmsucIFJeD0oNpLWX85je/ITo6mmeeeQa73c6AAQP4wx/+wLvvvsukSZMYPnw4M2bM4NVXXyU5OZmSkhKWLVvGjBkzGDRoEI88+ih33nknU15/nRCHmU8//piiymoCLRY+fvQ3DJ0xmx6jx5/UWycIlzrxL1oQOpnsclF4IB2X00Fs7/5o2si02pawbt0Z46Vh49YfGDRoEp5az/Pc0nNzoYchHrjjVgpzj5B+6BAZxbUcycykoWkPcdHRbN64BpcMSaH+DOzZg4N2PW/uLGi5d3iMP8NCfdEiMcA/moe3rMRanE6UdxTX9LwGo8F42rp/zF6EQe2B3VXLkXIjKWFxDLvhZnRGI2s//Te7flxCbkExPiOupkT2x+ZSWP/DF4SGR2DwDiC8l7sH6rPPPmPZsmXs2bMHjUaDRqPhyy+/ZMSIEVx77bVcc801LFu2jKeffpq7776byspKwsLCGDNmDKGh7hw0c+bMIScnh2f//GesViszpk/npvhurN2wgbqyUla8/xabv/2KoTNupNe4q9BoL/zeUIJwPohVQoLQiSryc2moriQmte9Z77wryzK79q2lsCSbmOhkBvce17mNPEeyLLM4ezElRzfx4MTXL2jdn/5vAXHx3fE06omJjkWNC4NGwtPz1MGdLCusyqtie1k9kxMCWZabzR1JcXgbDc2Tmd3DWmpcaLHjHutSAIVlh44wpU8vthenMSnhOlwuFwf2f02vnnNwWGxojXqObFnPT++9CYpC3MChTH/kiS4JEuxWC/tWLWf70kVYTHUAePoHMOT6G+g9cYrYCVq4aIllzYJwnrmcTmzmJmxNjVgaGrCbm3A6HHQbPKzT6lj4w/vcMO03nVbeuagwV/Bt5re4FBdXx11NyYFvGDP66QvahtqSbGpLc3HK4JQlXJKGg/ml3Dz71pOurawyszujipqiUvx1ZgBW1dUzoHsY/mEhzSEJoLgXcu817We2j7vTWVEACaxqDU1Gb2J9YonyjgQgd8kynPoI9P4euGxOFKdMSf4Bdqz8CkVxEdunP9MfexqtoWsCBIfdxv6fV7J9ybc01VYD4OkfyNjb7yZlxJguW2UmCKciljULQiezNNRTnpPVkmFVrdai9/DA4OVNQGQUeqNHS6KzzmIwdv2w0Pqj69lXtQ9fnS+/Sv0VOo17iKvUcOH/MPCP6IZ/RLdWx6qbVmI1N1JWlM+2A/mkbcpljTqRcJeK30gmusd6kXTtAHwSIpgK7Fq/CVV5Gf1HDW9VjiO3mKS44UiS+rRv6p7hHoQO6dfqWCKpyH7B7F7wTwr27eHbvz7LzCf+jMHTq7Mevd20Oj2p464Clyf56TsoztpDU201y95+jfRVy5gw7z5C4hIueLsE4VyJgEUQTqPo0AHsVgtIYPD0JqZ3v5NWYWQdPUjG5p8ICImgf+JwtGoNkqRyJ2CTJGQUFEUGRcGlyCiyjEt2gaIgoyDLLhRkZJeMoijIioysyCiKTENDbZc8t9lp5quMrzA7zQwMHciD/R886ZqL5e/06MhIVqxeRd/UXvTI3czA2n28Om0ahuFXIfmHk7PyAB4R7uXF33//PZaPF+Dl68uGggYWmTywofCnKSloiWNh/nZ0quOdzs0dLciomRnn7jlTa/U4bI1o9a2DEXuuFwNufJj9P/6LkswM/vfck9zw1PN4+vmf99fA5XByeNMu6orKQQK1VkP3MUMYMG0CTrudnUsXsW3x/yjOOMgXTzxE38nXMGL2XIxep9+QURAuJmJISBBOo6akmNrSYgD8wsIJjIxudf4f3zxPg6QmOKYP1ppyyqqPkOgfRaxvaPO4AkiKe46EJKmaPyRUKhUS7s8gIakkVMfOq9xJ4lSSCq1Wz4Beoy/Y8x6qPsTqgtXoVDrmpMzB33DqN9uVaS8yefQzF6xtZ+IoyKTk17fi0a8Xhl490UQm4ijMRm6ow+/3ryDLMo7iKnYs/JFhv7kVjUEPQEZ1I1aXTJPdxfdHyon0NWBzyrhkhendQ+kZ5MWS/E1Mj3Mvk7ZWV2CuKSIgaUCr+stLGqj423/J87WRe2QLLsWJp38Q4+/+Pd0G9EWt6bzlxk6XzOGt6ZjyjgIgqVXEDe1HRELUKe+pr6pg/Rcfk7nFvWLJ4OXN6FvuJHXCVafdoFEQzjcxh0UQOlltaTG1pe7dff3CIgiIiOQf819DFZtE37A4JiT2A+Bf25YiSe43gAMVh0kNSTlehqWBJ0bf1ByonFphbSFbsrdwtPYoj0569IzXnwtZllmYtZDSplLifeO5Nv7adtW3cfs7jBpycs/LxcSZe4D6xf/lkG8ftPjge1TCMiSF/tf2bPP6Y78OJUnin7sKqGyy8+TQOH4o2sqs+OFUp29FtjvxCI/CO6bbSfcX/d/f8L9xCg3B3nz3t+doqq1GZ/REr/ciqndPjF7e6D29MHh5Y/Dycn8c+97TC72nV6vlyFaLlZ0LliNpNSArKE4XkkaNSiUR278XESkdH9opPLCPNZ98QPVR9yqqkPhEJt79GyK6n37LA0E4X0TAIlzWPtz3ISkBKailk/8ytDqtGDQGPLQeeGu92V+1nxCPkFZ75DhcDnRq91yMtvbOURTltPMYrJU1WCtrkTRqpoy5lYeX/ZNHRtxEYmDrHY1l2T3f5VgA8M6WH/jtkKvR/GJYSVEU9pbs5XDRYQDCPMMYlTyK5YeWMyFpAl4enT8XorypnIVZC1sm0XbzP/kN+HQu5oDFYbdSVVaE3SVjs9moXfYC/X/3Bbp2LjE/Zv7qLALKLfjGuYhX1xLQsz86H3evk72ogqat6ch2O3J9I2pf9+8dxeUicO40tv/lXXZlrMaMo0N1qjRaNFo9Gq2BbgPGEhgdx4BpJ6f3Pxeyy8XelT+y6ZsvsFvcE5J7jpnA6Fvn4eXf/g0uBaEziIBFuKxZnBa2lmxFrVJjUBsYEDqg1e69iqJgcVqot9fjpfXCS9f5b/iZlaVsPXoYlaQmoyqHqd2GMTr+9H+lHiw/yoKDG/DReaFSWwnwtKG1uJfA9gjsQZ+EPq16NzbnbCbMM4yEsM6bJLmmcA0Hqw/ip/djTvc5LZNoO2rj9ncYOeiBTp9o3Bk2/20mviPmERASg0ajQqs34hverUNt3b08m1qTDWeFGWOUF2Nmuf/bmvdlYj2Ug6O4nKDf3oy6easB2Wqj9E9voE/pTtDdswBwWK3k79+Dpb4ea2MDtqZGrI2NWJsaaSzIx1pvwmk0ulebmZtOakPCwCHM/OOfOuEVaZvZVEfaV59xYN0qUBR0BgPDbryVAVdf1yoTsyCcTyJgEa4YFqeF9UXr0al0jI8eT1nFfvJLd+JyNCEpUsvsUJUkYdT54OsdRXx823+xltTX8MmeVfgbvFBQ6BEUy/jE3idd53C6WHRwK2/v+ScaScefRz3KhMS+Led/2UNTXltPXlktO7PL0KjgzY1l9Oy1mBgfPyobK/n7NX8n3C/8pHqyKrKoNlUzLOnclkqbHWa+OuyeRDs0bChDwoecU3kAew5+TfeoUXj6nnreRFcpL8qhcOdydD6hhKWOxc8/AL22Y2sMtv+cx5CJ8Scdt+zPxnIwC2QZW2YOlj270UREEfy7u2lYtRldfATaqAg8B6a0UWprzro6ar/6GlCwl5bi99ij2C1makuLWfzKcyBJ/Ortf+MbEnbGss5FWXYmP3/0PmU5mQD4h0cy4a77iOs74Ax3CsK5EwGLcMVJr0xnT+b3hDfVMmHUM2g9WndtO1x2rJZaNu1+n6lj/txmGQfKC3gx7d/oVUZ6BnXjy6x3uTHh10hIyLh4ePgsfA1elJpM3P3DU+TVVhFimcJV/UORUKFVq9ieX4HdZCBE48mOUju/HhqKyeIgMsCL2aNT8TA0D0UpCqV1pWhUGgK8AtCoT35DNVlMbDy8kWv7X3tWr8mBqgOsKVyDXq1nTvIc/Ax+Z1VOW46W7EC21hObMLHTyjxrigLmaqjNB0sd5U0KOytktC4z1so88vNzMY5+gLhQf2IDPInwN+ClP30Pwvbv99EzwInK2wdneTnO2gaQZY5vogTamEhUWjWW/ZlIKhXOmhpsOfnYjuwn4I67kPQaNP7eGPv3Qht8+tVC5j17aNy0CZxODH36sCJtFYWH9jPoulmMnXt357xOp6HIMgfX/0za/E8w15sA6DZ4GOPu+NV5D5iEK5sIWITLU9YqTnzDaEXBvftx7AjQnjrV+s9pfyPCbwhagwdaoycanQE1ElUNTby1bTljXGEcbajDGenCMyCWx0a7u/fNdgdzFv6BewdOR6NSk1lVxH+PvIKx/EHmDY0mIiiI55YeYkC8lh25Zmb1iWDpxiZcKg2f3T2ElJhQdB38K19RFP6343/MGTKn3fccm0RbZi4j3ieeaxOuPS/JwsxWExkZCxjY/1edXnabnHYwHYW6QpBdJ5/3DAT/ODD6U9lgo6LBSq8IX8D9OlY32cmvauJojYXKBit2l9yybLnls8VJVJWdGGshAUdzCbv1ehSbFU1IKNpQPyTN8SEl2emi4PaH8ZkyEUmrwZ6fh6FPH/yuG9tyjeKUMe0rwZp+AHN2OUqiP8bEaMJG9UWlbnt4SnY6saanc/DzT9hQmofB04tfv/fJBctUazM3sXnBfPb8tBRFllFrNAyZMZvB029Aq9NfkDYIVxYRsAiXp+JdYCqGhHFwlonLqr98nvrUschqNQ6rBafDhoxMSUEx9l2lqJqa0A4ZxNiQDAwT/w+HZKCqtJLw2AiabE6Ka804ZBc2p4N6i4LNWs+m3BrmjoxHq1GI8g2mpsmKl0GNxezAIcuUVjewPbOIe6cOQt3B3XTfXvs247uNJyU8Be1p5hVUNFXwbda3yIrMNfHXkOB3/pODbdzxDqMGd/LEW5cDjm4Dh7X1cbUGfKPBLwbUp+8dcbpktubWMCopqMPVL1uVTXy4D7HOWkzfr0Tt54uhVzKeI/qiNrqDBkVRqP74O+QmM3JDI7LFgufwgRhSEtAnRLYq7+CftxB8YxIqjYqgHgHUH8mncut+UBQ0nkYipgxH6+OFLMuYN23GmnEIxemkfvlyfg71oaHBxFW//h19Jk7t8LN0hMPaQF3GGmz1RQCYahrYnZZBRbE7W65vSCgT7voNCQMGn64YQegwEbAIly+rCdK/gaG/Pqvb7TmHkLM3Ix9Jw+P3n/LNr/6Ib0EmZv9gNMEhBI0aRuU3C9A0NRBw9z3ILhfFm7YR+cz/HUutgqzA0qxyon3cPTml1WbCAz1osjvx1B3vRZEVhRjJybYDBWgkmcn94hnbt32rcWSXjOKUyS3LIa86H72HgbEpY0+6bsPRDaRXpZ/zJNqz0WkBi9MGpelgaU6UFzsS9Oc2UXp9ZiVjuwef1b0HDlWQk1fLuJGx+PoZsGYVYtmXiWy34ygowGv0MOoW/kjU66fPQ1O5r5KG/HoSrk9s87zV1ETpis04a0xEzxpP1YvP4TV+PH4zZwKwc+ki1n/xEUExcdzxt3daesoUxdWydP5sWGuKqcv8GaetoeWYSmPEL3EUHmHdW44pikLm1o2s++zfNNbUAJA4aCjj77xXDBMJnea8pOZ/+eWXWbRoEYcPH8ZoNDJixIiWLdCPKS8v5/HHH2flypXU1dUxZswY3nnnHZKSkk5b9sKFC3n22WfJyckhMTGRv/71r8xs/qEVhFYMvuBz8gTV9tIl9oTEnlgsZhTZxZz//I3S7EK8Av3w9m/+YbnlOgAWvPg3NIczsCZ0wyWDSnKPOqkleHhIHJFep++mtztddHtvPT9c24M+CRFU5ZVzaN0eXE4XvuEBxPRquxdk9/KtFOTk0aNfL1BLlJYWcO1Vs1rOW51Wvjr8FY2ORgaGtJ2J9oJw2iF79cnHj23G0/qg+5h0wrfHvlBrIbwfGP3OTzs7KLVnCD2Sg1iXVoACjB8diyEpBoCab35ClxCDLi7utGXsf2sPel8dTqfMwdd2oAn3whDqgbPJgdxgRxfigdPqJPrqsai1Cjn/WUT4H55BXV1Mxt/moER7EBgwCLVGTVVhPnu+eoGQyMDm0lVA83waRUGRnSjIBPWbhUdAbEsbZFnGXHKI+rzNyPLx5dVaj0ACel6Nzuf0AZ0kSSQPH018/0Fs+fYrdi/7jpyd28hP383QmbMZfN0N7d6NXBDOVYcClvXr1/PAAw8wePBgnE4nTz/9NJMnT+bQoUN4enqiKAozZsxAq9WyZMkSfHx8eP3115k0aVLLNW3ZsmULc+bM4YUXXmDmzJksXryY2bNns3HjRoYOHdopDypcZmrzoWAzxAx3RxBnQ6UGSyN4+hLeLabNS65/9HdodQay5k7CWnniaiGFqnKoOmWC+mPvxhJvB6ZhWr+UHTt7UFKVRWJCN1Cnkrs/C5vVhlajpTTrKDIyTruTEdeNQ6fTMvN3t7SU9r+v52NtbCTTUcOK/BWoVWpmd59NkEfHhzw6VcxQiBzVtW04T9RqFRPHxWOqsfC3/+wkwKhlQM8Qeg/oSc0nC9DFRp90j9Mpc/hwJf5NCjo1GCO98OnmR2NGDQ0VZgJ6B2Kvs+Ob6EvOV0dwZdeRubOcnn8eTtIDN3Pwow9Ra8vx6TEa/yFT0QVE0qtAYd/PP1FUpmHArQ+csr2KLFO68WOqnCZU6uNzTYyBiYQOmYta73HWr4XOYGTs3LtJHTeJnz96n6MH97H5f19ycP3PTLzrN8T3H3TWZQtCe53TkFBlZSUhISGsX7+eMWPGkJmZSXJyMgcOHKBXr14AuFwuQkJCePXVV/nVr9qenDdnzhzq6+tZvnx5y7GpU6fi7+/PV1991a62iCGhK1D2anA5odsk9/yGDrJ+9zoK7tT4JwYYAIrdgn7sbWR+9h6GjfswxhnYd+8jaBUnpS4V4WqZD4uq8HfU4+kVQKpe4c6RM9qsp+7T4agH/gpjym1UHFyJpamIwKgReIem4myy4XA4MPh5IqlUlBwuoL6+nsQ+yexJS0PtpUOt0eCwO9hTu4ew1HimJ04/r5lvO2Jj8UZGXaQByz/XZNEr0pfxySHnXNbP6/J5cmUGs+OD6ZvYevXZnooGEv088NGp+XpPMaU2Bz6eOuxOF7cE+5Ne10SSrKGHSgN1NjwGhEC9nZp6G4f2VtNjTCRhib7E9wlCZ9SQtW8barWGhF4DAagszOez//sdkkrFr975Lz5BZzfM1Rlk2UlFRRbpa1aQuXoT9iYL4F5NNP7OX+MTfO6vtXDluSC7NZtM7qVvAQHuH2CbzQaA4YRt1dVqNTqdjo0bN54yYNmyZQuPPPJIq2NTpkzhzTffPJfmCZe7bpOgvhTy1oPDApEDOzRUZJjxKLV2B2XmJnDZkBwWcFrx2PsFVksDtRsWYQ/35/A901EHhzPWx4/EGHcgvjFjC6ONLt7XJPAv30ZiIpOxLfsUpboQw+3PtqpH6jULxV5P4+oH0Bp8wG8C9eWHMBXvI2rwLeg1x3seo3u7h4h2bdmIxltPU14N3knBDBs3nmF0brbTy13vKL+znsPySxPHxbF9XFyb59SHy6lssKHxMTAnNInJvdxzO37OKKfUZMXPpGdgahi9InywmZ0YPLWU5ZmI9dVjVmVhszixNjooPFRDt4EhJPUZSlHOQfZvXUtcrxBUXrWEJUVTlnWU9JXLGH3rnZ3yTKciyy6qq3MpK8/EYbfROqBXERgYzfgb72X8zF+z+duv2L1sCdk7tpKfvpths25m4LSZaLQi6ZzQ+c46YFEUhUcffZRRo0aRmpoKQEpKCrGxsTz55JN88MEHeHp68vrrr1NWVkZpaekpyyorKyM0NLTVsdDQUMrKyk55j81mawmQwB2hCVcgn/DjQcrBxaDzBqcFYka4l7meRm5FIbWrX8Q/ZjB+4b1AawCtAWXwPDy0HgRKEpKkZriXP5rm1TlOp52n1nxLoVPFoDwNb5obiPBvJHDfjyzzr2SvXzVTlr3GALMRVVAUcmUODvNWGvxcaCQjmvBZRPS9lrr8veQdfp9YzR1ttq3iSCZytJY6XQ0V29fy0/L36TlsDI3menfq96CIk18KnwBGDz67fC3C2fP30NFgdZIS5k247/Hl9CnhPuRXm+kT5cs7a7L483W90GlUGICwePdy6yHTEqivtiC7FNRaFQUHq5vvDiMgKACdXouXVwpDrlPx/esvkb56KQOvn4KH17lNeJVlF3V1BZSVHcFisZxwRkKSJPz8IuiWOAaj8fS7OY+7/R5Sx07k54/epyjjABu//owD61Yz8a77iOs38JzaKAi/dNYBy+9+9zv27dvHxv9v777Dorryx4+/pzEz9N5ExIooKmqMXewlmpiyiWmmZ9M02WSz+012k1+ym8262U3b7CZmU40pmkRji52oqLEr2AUUFUGa9Dr1/v5AEQQUEJhRPq/nmUe4nHvmc88D3s+ce8rWrdXHdDodixcv5tFHH8XX1xeNRsP48eOZMmXKFeu7dJ2IK+3lMmfOHP7yl780N3xxPep9W9W4lpPxoNJA9WwZFfj3AO/aYw66BIbDvZ+QfGIXp3JP0CvmFgyG+sdZXaDVuvDPifeyPuEMv5gL+MFu57EDpehtHZlwa1emRXih8dGjVqupSMqnKKEjGu9JeKis2MKLKM7fS+aBqt/rwKCGp6lGBUUTMa5pq9H+/Mv8JpVvCaoGx/C0H/3DfegS4M7z3ycS2yOAB4dFAHAyt4zpMaH4u+vJKKzglv/+ym39Q4kM9uQ3A6tWB/YNdcM39PK/c1A1M8fDL4CSvFwOxP+ExdMHNzcDlz7KbNjFQdDK+QHRnp5BdOo0FDc372Zc9UX+4RHc9docjv0az6b5n1GYdZbFc16j++BhjH7gMTz95TGRaBnNSlhmz57N8uXL2bx5M2FhtZflHjhwIImJiRQVFWE2mwkICGDw4MHccEPDg7KCg4Pr9Kbk5OTU6XWp6eWXX+aFF16o/r64uJiOHesOghPtTKdhVa+a7DbITYLkdWC3Qo9JVQNuz+ve+QbKAyM5uHcpHr7h9IwaecW3KTHbqTRqiNQbCH+uByxIpmh5KmqDBl0HD7wmdUIf7kngU/1ArcZebqbyaD5BY+pOS76gLKeck/Hp6D1dKMmzEdHcNmhjV/pwcT1YdzgLve7y04h/P7FH9UJ1AF0D3Xjxx/0M7uyHXqtm0ZNDCfd1Ra1uelupNRr6TbyJrQu+ImVLElNefJWcnBwiIyObvK5Pa1CpVESNGE2XATeyfdG37Fu9gpSd2ziZsJchd9zNDdNulb2JxFVrUsKiKAqzZ89myZIlbNq0ic6d6+6zcYGXV9UfbkpKCnv27OGNN95osOzQoUNZv359rXEs69atY9iwYQ2eo9fr0etl1UXRCGpNVQ9LeR6U52EuzePwkcMUlpu5sLCKwaBHperAkYwKVpWe4oVBEZet8vbBnZhYYeFkdgmJaQWcDtJyR/dQNC4a1C5qSnZmYs2tQN/JE8Vkw3NCOPYKa516rGYr2Kr+to6sSCX69m6cMxUQt3IvAxjapMt0RG+Hm86NMktZq2wuebWKKyysPJDJ1L51xzXtOplPpcVWzz7ddRVVWOjgbWRgp8svrX+pEC8j/767P1abHT/3q/+/qs/YiWxf9B05J09gysulR48eJCUlERwcXD2O0NH0rq6MfuBxeo+ewC+fzyXj2GG2LviKw/FxjHv4KTr1jXF0iOIa1qRZQk8//TTfffcdy5Ytq7X2ipeXF0Zj1bPbH3/8kYCAAMLDwzl48CDPPfccAwcOZPHixdXlH3jgATp06MCcOXMA2LZtG6NGjeLNN99k+vTpLFu2jFdeeaVJ05pllpBoyNHMYs4m70VtKkKlNaA1uNO7d198vOr/Pfn3nlM8d0NEk97ju91nGFxor15YTqWC8rxK3INcCenojjmjFJcuXlSY7fh09QbAarGx+7NDBPX0RQUERPmi8dKwMj4egzqTaRMfalIMC5d+gKuHJ/17j6RjcP0LlTVWQWUBp4tPY7VbsSt2bIoNu2KvUy6jNIOx4WPxNzp4enU9Tp4r4/DZIjwMdT/Z+7q60CfMq56znNuaue9zeFMcPYfHMvXZPwCQnp5OZWUlXbt2daqeLkVROLp1E/Fff055USEAPYaMYPQDj+Hh53y/L8JxWmWl24b+GL788kseeughAD744AP+9a9/kZ2dTUhICA888ACvvvoqLjUWFxo9ejQRERHMmzev+tiiRYt45ZVXSE1NrV447vbbb6exJGERNSmKwvtxKfQP98ao0zC4y+UH4Na06kQuKfllDSzvUncKNMDhlDzmzoipNd1YURSKThdzLrkQFAWb2YbR14C5rKqn5VxqEa7+BkpzK7BU2nDXqUn1zOWWe2M5ceoAqaeP1Ok1KSk8h97NA3NlOTablX79RmIqLuHXt96lJNKHmNhJ9O87ipCA+teVaazN6ZsZEDgAtUqNVq1FrVKjUWmc6obYHmWfPME3Lz2HWqPh8Q+/xN2nqmelvLyc1NRUOnfu3OB6V45iKi9j2w/fVu1NpCho9XqG3nEPA6dOl8dEApCl+R0djnCw9IJyDqQXkZxdwu/G97jyCc2w6Uw+h3NLsdkVNu87zqBTiUT8ZgZWRaHSqmC227kxzIuBHet/lHA87jRaFw0dBgWhQsUPby2j04jjeLkbMJ9LAr0n6N3pM+QFdC51F/1auvZzTnyxBLOLQkTP7oSMv4PySgWjwYC/lw9ncjLpEh6GxWzl+Mk0DMaqxxIqywpcfP3QunlQYS5gUvSf69SdVZbFicITnCg8wT1R96BTy43FWSz4f3/kbNIRhv7mHobdeV/1cUVRWLp0KaNGjcLPr/EJelvJOZXKL1/M5WzSUQB8Q8MY9+hThEf3c3BkwtEkYRHt2qGMIk7klqLXqtHrNJzMLaNHkEezNsOrz1eHMlh+JIsFt8egVql451QWf+wSUqsHYn1SDjmlJu4bWP9g8FO5ZWxNyuHUqWLM54p4crAboUOrpoIqdjsqtZrSwjSOrH0R7z4zCOk4FIuphOKCE7h5dMCu9WPbntWUFhVg00LnVDVdew+hOCqYSmslA7pHs3zDBqxmO+NGDMbLvWqKqqIo7N/+DZH9bmF3xreM6vF0g9eZW57LsfxjVNoqmdBpQou0nbg6x36NZ+UH/8LN24fHP/yiTi9Feno6paWleHt7ExzsXPv9KHY7R7ZsJP7rz6koqVqKInLYKGJnPoKHrzwmaq8kYRGihtwSE19vP8X6ozk8PborN/eru45JTXlFpSzYfBg/j9p7BYX4uBMeEcL9C/fRu4MXn06rWq7/+8x8zprMAJRYbdwZ5MvB4/nkVpjxN+goMFnxdNFSabaBXSHPZkOrUvHc8Aje2reIse5+DO45rt5YLKYSKsqyObHvM1RaA2FRd5CRtAJDZj7G5GMUhsVQ6d0RtauRgPKO2ErKcOvRmcDBvRp8hJN9JomykizStUmArSruiiw8jEEEeUUTGTSqVvlfM35leIfhV2xn0fpsViufznqEsoJ8bpr9IlEjRtdbrqCggMzMTFxdXYm4wr5Hba2yrJRfv/+G/etWoigKOoOBYb+5l/5TbkGjvar1TMU1SBIWIWqw2xU2HMsBIK/MRLdADwaEe9d7Q48/kMqx9DzuG90Hd9faCct/lm2jzNeHM+V27FY770/qhV5be1rprqJSCi02JvpfeVBnflEu+04fZnzf0XV+ZrOYOZeyC2wW7FYLpsoKbFYrnYfdilqjoeiTN9C4u6Ny0WMYcysav6okTFEU8vYlUbAzkS4P34rGWP8GjWkpeygqOEGXnpNw8/Rmc9JHoFLh5RpOv7DaC9BJwuJcti9awLYfvyWkeyT3/u2dy5b9/vvvCQ8PZ8CAAU43szL75Al++XwumSnHAPDt0JHxjz5Fx959HRyZaEuSsAhRD6vNzv82p2LQaXhkeES9Cct/lm9n9i31Tyn+39ad9AkLYVhEOM8tTGBqnxDik3MZHRnIhF5V6wYdLinnk/RcOhvr3hxysssI1mjIKzNjs9npojnJwyMnoapnb6DCtKMUZKTg1yUGtU6P3uhGbvIeCrJO4XJ+gTuVCqwV5ZhTDqP4D6Tf3TOqzzeXm0n7YQ1qrYaLg4VrXq+Cgp0y+ypCY6ayyrybSpcO9V632Wamf2B/Irwi6OBefxnRdsoKC/jk6Yex26zc9/f3CO7a/bLl7XY7SUlJhIaGVi854SwUu53D8b8Q/+2XVJ5/TNRzeCyx9z+Cu6/zjcURLU8SFiHqcSijiMQzhdzWvwNu+vq7nr/bmMi9Y2Lq/dmifQfQGwNZeTCTNfvOEt3Bk/dn9Of3PySybNaVNwH89/40nutXNYNnzS/v0c/bRmrOObxc+mAyKxSUVtIpqOqGYreYCeszArfATo26toNLl9Ln1lsvW8ZqNZG3fwWWkkxADSpwDeiOV7eRrD0xj5uinrrs+dLT4jxW/edtjm7dRK9RY5nyzAtXPgE4duwY4eHhuLo2f+fm1lJZWsrW779m//pVoCjoDEaG33UfMZOmyWOi61ybbH4oxLWmh58bqVsy+eXcKdQ6NcUlJnpHB2B01aJVq9FpVJQXVZKXU8jRPacACO8eTHj3qsGLpaZKymyZhAab+WN/I8GFpXzz6efEDujdqPfXqdW8vz8NbeF2bul3ByH+4fiZizlw8DuCg4ZSnJLLGauCRuOColHIOnoKjp7CRatj8JAhqDWX36XZkp2NJSsbe0U5ZRUaykwaLOWVYLFgt9mxqYqxdchD694RT59gwroMru5lqrSWsTd9NQPDrryVhnC8/pNv5ujWTSRt20zs/Y/g6uV9xXMiIyM5cuQIXbp0qV47y1kY3N0Z/+hT9BkzgbjP55J1PIlN8z/j4IZ1jH34CZlNJKSHRbQPdrtCakIuBjctId280WirbvyVlRZSUgqw2hVsdgWrzY5iKsNTr8bb3wOfAE82LNpLxx4BhHcLxifAk6wz5zh+KB2tVkNehZliXzfuGdGz0bEkZu0mqwImdx5UfcxqNXHqdBw2m43c3H107/Y4ri4eqO0qCs+co+j0OXRpp1CyTqNEh1U/2am5Kow2Jxdffz/U/oGc2nEKl46hdJ46GBdPN9Q1dlAvKzKRfbKYrFPpVJiSCOpUlYxtzdlI3z59GNb5Nw3GvjVjKyM6XLknSbSNb//0PFknUhg+YyZDbp9x5RM4v6rykSN07doVg6H+8U2OptjtHNy4ni0Lvqp+TNR98DBGz3wMzwDZm+h6I4+EhKihvNjMvnWn6Te2Ix6+TftPuriwFIAje06iKGC32hk0rhcuLjrOphSwb2cG/j29KT9XgZu/kYAgD37dk0VmXjndo/zQalTYga4dPTEZiok//hU9PWs/m1947Dvu7nkvACVZqfhm3UCvzn1RdC4oGujQpzNqjRrTySzK9yZVn6cN8MZ1QDc0HlVjWuwmExazlZ3/WU9pvgnfQB3RT04mJzOPwrwSspMrCAzxo0N3Hzz9DRjcddWL3SUcOUpmbi4VFZUMuyGGkHo2rZNHQs7lyOYNrP7wXdx9/XjsP583+tGJoigcOnSIHj16ON1AXEVRsFqtmM1mSgoK2LPsB5K2bARFQeviwo3T7+SGW25H5+JccYvmk4RFiBqKz1Vw5mg+vUe27IDRo8tTibqlS61j2/dm4OWmY++uQ1SaNPQ0+BEW4EbGsSyORZXz2KRx5JYWk3IuG7tez4iQ2qvS5heksmzrW9yjH4NK64LKYEAbeQNqv9oJhKIoWNJzKE88yd60E2jdVPj5eaBRqbBarWSfU+HaKQyDqxH/YB+8/T1IT82m4FwhRlcDA4bXfoxVWlHO5l27sVkUxg0fjGs9jwwkYXEuVouFT595mPKiQqb97iUihza+98tut5OSkoLBYMBgMKAoChduBzX/bc6xq72taLVaXFxcql/FWWfZ+u2XZBw7DIBnQCCjZz5GtxuHyurL1wFJWIS4RHmxmT2rT+HhY6D/xKtbuh6qelqOr0+jx5SIWsetdoWtv57GVFSIolIxfkof1MDu+E0MHBXLh9vicdfriAoMZl5KPi5uViL91ZRaLbhptLiWZdHDfJpRQ6o2A7WXlWI9uhulIAd1YCd0/YbUer/k9VswF5WRcGw3vUaNpEvvPhhcjRiNDQ+s3L/zGGazlQHDeqHRqCktLWfZhl+4Y+JEDIaGP7lKwuJ8fv3+a3b89D1hUdHMeP0fTT6/rKwMm81WfeOv+W9zj6nV6hZPJBRFIWn7FuK/+YLSvHMAhPeJYexDv8Uv7Or/noXjSMIiBEDWQSjJqtqV2b8bKdtS0WpA0wLDzcuOJdGxewguBjVcZt9fxWYjqVJNesdOJObm8Pjg4QS4X/wdfTU+mb+O6l79H/yWgxs4V1KIn5sno/qNr66j5PRSKlI2YAucCCo1vhGDMXgHcuLgSdYfPU4ZhdhsNsL0WrxLi/H09at101Cpq75WlKrp0BUVZjJOFdFtSG+yi0sZMSSGAO/LTyOVMSzOpyT/HJ/NehS7zcYD//wPAZ06OzqkVmWprGTXsh/ZveInbBYLKrWa/pNvZtid96J3da59lETjSMIiBMDxOPDrBt6dIO84eIWBrgVmRxz9Gcxl0O/KAx0zju1i9f59FK9LZeSUsZgrK7EUl1K5fi1jvv2EE+VW/pdwhvt6hzIoxAuVSsX6g/GM7j0KSk9TdHIRimLHM2wKev+qlXXtdht5yb8yv9BCoD6I+2N6o1KpUBSFRQn78dCoGdUzEtfLjE+wmE2UFhazNO5LHr73j426bElYnNOK998iefsW+oydyMQnnnV0OG2iMDuL+K8/4/juHQC4enkz4p4HiI4dX++6RsJ5ScIihKLAyhfA1R/G/IkGtl9uuh0fg8ELYu65bDGTxULy3s1YLRV0jRrOjm+WYdqzC7tGS8CEcagNBuwWCyqVinO5BZSEhXPML4QKiw296w4ilTNotG6UF3ek9HgWgwJd6GywU3NukFuvYayyFXNP15EA2Ox2tq1cQ7HFytHDx1C5GsHdHQCrRkN439rL9StAbnE2qjBvUF3cG/pMWSH3RcTQ1yeE5IJk/rf/f9za7VbCPMLo7HV9f4K/FqUfPcT3r7+E1kXPb+fOw3h+36j24NT+fWyY9wkFZ9MBCO7anbEPP0lI90gHRyYaSxIWIS44uAjyUyG2cb0IDbKaYcNfodd0CBt0xeKnt8ehWvEvsGqxqlywjn6SHlMm1Slns9lZ98+PUZ1JY/edDzLBnoPdamXYpKq9hbb8Eo8lL5+xd91Wfc6O7/5DZuUZKrz9mTjhAXxcA9i+bgN2m41uvaMI7Vx3sblTp9NIS9iPR/fu9OzRDaOu4ediZpuVj4/vYnepBl+DBz09/HkqXKaTOitFUfj6/54l9/RJRt33MINuucPRIbUpm9VCwpqf2b7oO8wVFQD0jh3PyHsfxM27/t3ShfOQhEUIqEoyjiwFo+/FYyoVdBkDTek2LjgN2/5T1VPj6ttgMaWilPLV87GXlaMN7YQ2vCtqDz/UgR0b7Ka2Wm3E/eMjzqm1VAb54ZeUiDUwmNBeVWu7nNt/AHthIW5RPZn40P0AnDhzltyjR1GUizM1fEND6Nn3ygvY7V+znrOffMKUn35ssMzpChPbCksZ5+tJoF7XYDnhPA5uWMe6/32AZ0AQj37wCWq15sonXWfKCgvYsuArDm+KA8DFaGToHffQf8rNdXa1Fs5DEhYh6pN9GM6lgEcIhA9u3DkZiXBsBYz5c4NJjnlvHKaj+1HpXDBOvg+NV8NJzeUoioLJbMbQCmtj7N24mcryCrJ//pmA/QkM37qZhdkFeGk1lNvshOh1jPDx4K3UTHLNVqYHetPHw4j3ZXpihPOwmE188tRDVJaWMP3FV+g2aMiVT7pOZaYkseHLj8k6kQKAd3AIsTMfo+vAG2UatBOShEWImiyVsOPDqsG30Xc0bTzLpn/C6LqPk+z5mZSv/R7FasUlejD6/iNbMOCWl7B9J+UFRehdXblhdNXA2cOlFcTnl9Db3YjJbiet0oyrRk2ATkuB1UYfdyNR7s61hLto2OZvv2T38sWER/fjzlffdHQ4DnVhU8WtC+dTVlgAVE2DHv3AYwSERzg2OFGL7CUkRE1aPcTcB8Vn4cQvdWchK7aqQbpBvcD7kjUdFGuNYjYqNy7CmnkGtYc3brc9icpJlze/VI9+fdm77hfKbbbqYxmVZjy1GtIrzfR0M7Axv4Sh3u7kW2xM9vfES3pXrikxE6eyZ8US0g7tJy89rV2vT6JSq4keM4EeQ4azc+mP7F25lLSDiXz9x2fpO34Sw+66H1dP59q5Wlye9LAIcYGiQM5RKM642AOTdwIK07BG3kPF5tWACsPIaeg6N37vIGexY+0vlOTkENanN1ExfauPn6owsTAzn/F+F/9uymx2Rvm4S/f5NWjZ23/j+O4d9JtwE+Mfe9rR4TiNopwsNn/zJck7fwVA7+rGkDvupv/kaTK+xcHkkZAQLSDPbKW0LJ99K37GJTwSjUpNqMpOBxT0l7mXH08+gl5fu+dFAXJyCvHx74VB79LgueXnFDoFq+t/anXhr1UFismC1y1D0bg1/pHNhh9+Qmc0MvLmizsyK4rClxnnMNkVmQl0HUg7tJ8f3/gzOr2BJz7+ShZTu0T6kUNs/OpTck6dAGR8izOQhEWIZjLb7WwrLMVsV/DTaXHXaujhqkelUmGxK5w1mTlTacZkr/rTqbljcmejns5GF07v30dEzMA6dR/cuIeIvt3x8Gu4Kzpt/WnCJ9SdlnwpW3EpBT/8iv9jdadKX07SoaNkHz9B38kT2F1aiQL09XAlWGYDXRcUReGrF58hLz2N0Q88zsCp0x0dktOx220cid9Qe3xLdD9GP/i4jG9xABnDIkQzfZeZz93Bvhg0dWcE6dQqOhn1dDLWncWjKAqpFSbW5xVTYbITUU/ddru9eon8q2UrqkDjaSB/4Sb03YJwuyGqUed19vbER62QtHsfY0cMQSOfKq8rKpWK/pOnEffZRySu+5kBU26WlV8voVZr6o5vObRfxrc4OelhEYKqZOPXwlJyzVY6GVwY4NW0bnRFUZi/eSsurq4Um8wM79SR6I5hdcpt/2kLA6cMxOWSjQkztmRQtC0Dl24+YFfodmePJr1/2a5jWM7m4X1r7Y0JbSUlVB49ilJZWX1MGxyMoUfT6hfXFnNlBZ889RCm8jJuf+l1Ove/wdEhObVLx7e4GF0Zesfdsn5LG5EeFiEaqdRqY31eMeP8PPHUNn+xLZ1Bzz2D6j4Gqklv1Na7TWKHkR2wm20EDw9BZ2j6f5CG6E6UJ6ZQuuUA2lA3LGlpqDQa1O7uGKOjUbs2vHOzuP64GIxEjxnP3pXLSFizQhKWK/AKDObmF16uNb4l/psv2B+3Wsa3OBFJWES7llBQys68UnobXNhw8hznys2UW+wAKFwYo6Kq/u7ibjsKdlS46tQ81qcDarW6USuLWl1M7Izfj1Z38ZFSeXY+3f27o9Gp6k1WzBlZ5H25CH23rpetW+OhpWhNPCoXd9RGV3TBwfjc2Qu1sf5p17aiIqz5+bhERMh/xtehfhOnsnfVck4m7qUgMwOfkA5XPMdut1GUk101Y64l1Nwt/MLfTs1jqos/rfq+xu9hI3528e/xYmX1/S5XH7vCz/zCO3HHn//Kkc0b2LF4IYVZmSz71xsyvsVJSMIi2q2dqXnsyS+lg5cBFSp6+roTFOaCt0Hb6Bt4WnEF/92XxrM3RGBvRPlKWyU3jhyEwe3iI6ctX68lcEgARs+GekFUuA6+Aa8JjV+51G63Yz5xhvK9+8BuRTGbUel01cv4q1Qq1O7uaAMCKNu8GffY2EbXLa4NPsGhdI4ZyMmEPSSuXcmYh357xXN+fv8tUnZua4Pori1ph/bz9f89yz1//ZdsquhAkrCIdunNHSfIqEgnylhEZr6GTKj69KUotT+FXfikWV8Cc/5nKeWVfHQ0hfKigxzelXj5N7afIfVUB1z0F6cie0UGUXY0F5VP/c9ubeU2rFlnKN1irffnV6RSozo/xbrmVdjLyzGfTkPtJtNer1f9J9/MyYQ9HNoUx/C7Z+JiuPwUeBfDxaRZZzBeZc/bxV6a6g6bGj03F3owaz0jPf9zpebBC8eqD9Wst24dF+tt2eGZLkYjak3725/JmUjCItqVIouVjfkl/HZAOGnFKnbkmrEpNkx2G356V27w60JPr1C0mqb9aaTkbGdraS69B73QYBmbrZLs7J8JDe3TxKh9cIu5s4nnCAERffvjExJKQeZZjsRvIGbS1MuWH3HPAxzfvR1TeRkj7p7JgCm3tFGkbaPWHJN6k6NLy138maqRj31F65FZQqLdOFFeSWq5ifF+nvV+cjxnKuf/7fuWSM8wnus9pZ4aajNZy9h0/FsqbRV09YshOrj+xyqKolBYuBubrQwfnxGozCrURvmsINrGvtXL2TjvE3w7dOShdz66Yq/J/vWrifvsQ3QGIw+98xGe/gFtFKlor2SWkBA1nK4wkWWyMMG/4bUVNCiUmUvZe+4wcDFh2XtkM6fOpWK2mUjJSSIyOIrc9CNo3PPpXBZM7+jBdPTuW2+dFnMJ2TvX4+U7AKPOC0teCSoXDUXrTuE5uiPaIDe03i2/M7MQF/SOHc/WhV+Tn3GGtIP76dQ35rLl+46bxJEtGzmbdIRfvpjLrX94VQZlC6cgCYu47uWaLaSc71m5nDn7FwFWJoYNrT52OGkXu05u46mpLwFgs1lR7AoarRZzZTEWUxlWi40DW74Du5WAjlGEdYpGYwikJCuZilM5hA6+GbVL7dk/AY/0wXSyCKXSCkjCIlqP3tWV3rFjSVy7koS1K66YsKjUaiY8/gxf/99zpO7dReq+XXQdOLhtghXiMuSRkLiuma12/rbtBP6udffu8T2zhSjPcrQ+wQBkmHJwyT5KmTEKu0cHFBTKKorolOcFVjuj7p1W73tYS/I4tmQubv7BVFaWEBbpi7L3KEr3MXgNrX/ZfGteBZbcCow9fVvuYoVoQF7GGea98BSoVDz2wad4BQZf8Zy4z+eyf91KACb8djZ9xzVtCwghGkseCYl2z2ZXWHUwk1eHd0VXzzL7pZFe7P/u/3HDQzPR6430A7JWfUPwpPvrlI3/8jty929GUalR1DoMXn54deyGrTiHs9tW0HX8XejdAjDt2QsZNlym3YLG16dWHda8CuyVNmzFJgBcOnq0ynULcSm/Dh3p1Lc/pw8kkLhuFbH3P3LFc4bcPoO0gwkUZJ4l7tMPCegUQUg3mdIrHEc2mBDXJUVR+PnAWSZHB9ebrABkJ+2hx+2voK8xxVjl5o9SXlinbKi3DqOXF25uRjwMCgWJ6zm16jNS1i1AXW6Hw6cwHziEYWQsxomTqpMVxa5gSi2kMrkAa14lGk8XDD18MEb5oXFveMdmIVpa/8lVPYSHNqzDYqq8Qmlw9/HloXfm0nN4LIpiZ+3cf2O1WFo7TCEaJD0s4rqjKAqLdp1haGc/ysstlGNBAVwNGow1diQ2+oZSdDaFgIDA6mMaVzdMpeUYXL2rj9nKClFrXHGP6Fd1wGoi3N0b89E0FB1ou0Wh7dgBa14FppMlF+OgaoE2rZ8Brd/l178QorV17n8DXoFBFOVkc3RrfKMe8ag1GsY+/ARph/aTl57GjsULGHH3A20QrRB1yRgWcd05fSSP/WmFeLjrai39ffxoPsEd3WuVNWQcpUfHqkXVbDaFpP0mbnx8BIdSU6gwVWK3K5hTjuMTVY5ao1BkLSexNI3hmcO5ccAEVDpN1XtoVGj9jWg89S22G7MQLW33ip/Y/M0XBIRHMPOf/2n07J+UndtY/u7fUanV3PfmuwR16dbKkYr2pLH3b0lYxHUl83ghBncdPsFNX71118oTnCxPxT/ClX49euFpdEer1aJWq0jKTyKzLBMvvRcxATHYSy2oXbWoGnjcJIQzqigt4ZOnHsJqNjHjtX8Q1iu60eeueP8tkrdvwT88gvvnvCe7GIsWI4NuRbuTvOUkttIy/IIN5OZoUGnUqNRqFLUG327BqK+QXBi6WemlDadP14sDC5MLkskoyaCHbw9G+46uPq7xkPEn4tpjdPcgauRoDv6yloQ1K5qUsIx75EnOHNrPubRT7FzyA8PuvK8VIxWiLvl4KK4LpuPH8a7IILCjG2qVAlYz9rIyrIVFZG87SMnJzAbPzS7MZVncBvIKC2slK5XWSrLLshkTPoYO7lfe6VaIa0H/yTcDkLJ7O8Xncht9nqunF2MfeRKAnUt+IOdUaqvEJ0RDpIdFXNPsZjOl8fG4dOhA4MQR9ZZRubhgqTDVPddu5+eN8bgaDNw8ZnSdHpgjeUeICYy5/PvbLVitRVgsxZjN5/D0jEajaWjXZSEcLyA8grBe0aQfOcSBuNVNGkQbOXQkydu3krJrG2vmvs99b76LRiu3EdE25DdNXLPMp05hOnECj9GjUekafp7u1TmY4yt2knck/eK5laVkHNnLsD/Owt/Pp97zvA3e7M/dD0Bu3lZGdRhK1X7HNTZEU2nRaj3R6bzw8OhFfsF2AvzHtcj1CdFa+k+++XzCsoYht9+N1qVxjzhVKhXjHn2KM0cOknsqlZ1LvpdHQ6LNSMIirg1pO0GtBZsJrJVgt2HZthmPB9+44ql6X096Pzih1rG4BW8x4e8vo7nMrsxdvLrQxasLAD9nLuRsRRl9wm667HsdLi5GpSrCTt3ZF2cqzdwZ7ItRBuoKB+t2wxA8/AIoycslafsWesc2Psl28/ah34Qp7FzyA9sXLaD/lFswussiiKL1ScIirg35J6DHZNDqQaMHjRZLylbS4+c26vRz2XH0uX0BGm3VJ8nwqBtJ+PxfVHQORtWrO6rzCcbxwuPc0/MeXDS1P3GO7vMGu1Pns2jno3QPnU6/jrcAVWu+WK0lWCwFZJnK2Vaq5Y8RnmjrmdpcYbOzr7iMSruCxa6QY7bwQAf/q2kVIZpFrdHQb8IUti6cT8KaFfQaNbZJGxz2m3ATO5f8AMD+tSsZcsfdrRWqENUkYRHXBrcAMPpQY2EV1MHehA14qlGnlyzaD1YbaKseJYUUqugQMw63G2+sVc7X4MvurN21jikoKIqCznMg/q692Zn2HbvSljEwZBThXp3Qab3Q6bzZW2xmTMcRpJvMlFpt2IG+HhfHsxg1aob7eJBrtvBrQSn3hvg1vz2EuEp9xk1i++IFZKceJzPlGKE9ohp9roefP+Mfe5q4zz7i1x++wdXbR/YaEq1OEhZxbXBxhxMbah0ya2yNPt2v81jKUw6gZBWj79oNj9Gj6y0X5Xf5/7StdisDQm7E06XuWgETQ62crjRjVyBIryOlzMRP2QVoVOCu0aAGrIqCt07LrUH1j5sRoq24enrRc1gsh+PjSFjzc5MSFoC+46eQl3GGhNUrWP/Jf7CaKhlw0/RWilYISVjEtaLT0LrHzqnIy9tMzUGwVS70wlw8XumSB4dKCLzn0asKQ6vW1pusAHjptPTVXfyTCnCRhbWEc+s/eRqH4+NI3rGV2JmP4u7T+N3DVSoVYx78LWqNlr0/L2HjV5/i7udPj8HDWzFi0Z5JwiKuWf7+Yxtd1s8vFvq0YjBCXIOCunQjtEcUZ5OPciBuDcPuvLdJ56tUKmLvf4SygnyO/RrPuo8/ILhrdzz9A698shBNJNMVhBCiHYs5v4vzgbjV2KxN341ZpVIx8PyjIFN5GXt/XtqS4QlRTRIWIYRox3oMHoabtw9lhQUk79zWrDp2nJ8xZPTwJHLYqJYMT4hqkrAIIUQ7ptHq6Dt+CgAJa1Y0q47K0mKgakG6kO6RdX5uqawkL+NM84MUAhnDIoQQ7d6FheAyk4+RnXqcoC7dmnS+wb1qIPq2H78lZdc2htxxN+4+vmz78TtOH0ioLhczaRrjzu9HJERTSQ+LEEK0c27ePvQYUjW7pzm9LJOf+h033nonOoOR3NMnWfHuHBa8+odayQqAT0hoi8Qr2idJWIQQQlTv4nxs22bKi4uadK7B3Z2R9zzI4x9+wcCpt9b5+fC77ueZLxYyYMotLRGqaKckYRFCCEFI90iCunTHZrFw8Je1zarD6O7B6AceY/BtMwAwuLnzxMfzGXLH3Rjc3FsyXNEOScIihBAClUpF//NTnBPXr8Jua/xK0pca+pu7CYzoSmVZKes/+Q+KcunijkI0nSQsQgghAIgcNgqjpxeleec4vnt7s+vRaHVMeeZ5NFotqft2c3DDuhaMUrRXkrAIIYQAQKvT0XfcZAAS1vx8VXX5h0cw/O4HANj45f9I3bf7CmcIcXmSsAghhKjWb8IUVGo16UcPkXv65FXVNXDqdLoMvBGrxcyyt//G0S0bWyhK0R5JwiKEEKKah58/3W8cBjR/IbkL1GoNt7zwJ6JGjMZus7Hqv++wb/XV1SnaL0lYhBBC1NJ/UtXg26Nb46koLbmqujRaLVOeeaF62vTGef9j24/fykDcq3Rky0Z++OufSGnmdgrXIklYhBBC1NIhqjcB4RFYzSYObVx/1fWp1GrGPPRbht11HwDbFy1gw5cfo9jtV113e7V//WrOHD7A8nf/jtVsdnQ4bUISFiGEELWoVCpizveIJK5did3e/CnONescesc9jHvkKVCpSFy7klX/fQeL2XTVdbdH/SdNrf76xN6dDoyk7UjCIoQQoo6oEbEY3Nwpzs0mdd+eFqs3ZtJUbpr9ImqNhmO/xjP/D7M4fSCxxepvL7oPHl799er/vuPASNqOJCxCCCHq0OkNRI+dCFz94NtLRQ2P5baXXsfdx5fCrEwWvfkKq/77DuVFhS36PtczjVZLp779AbBZrWSnHndwRK1PEhYhhBD1ipk4FZVKTdrBRPLSz7Ro3RF9+/PQux9XDcZVqTi6ZSNfPv8kqQmyXktjTXnmheqvv3n5dy3y6M6ZqZTrZKh2cXExXl5eFBUV4enp6ehwhBDiurD0X3/jxJ4d9Js4lfGPPtUq75F5PIn1n35I7qlU1Boto+57CHdff1QqUKnUoFahQoVKrYLz/6pQoaoqUFXmfNkLP2vonKryF/+t/pqqwcFVFZ2v68L7q1Q1vqbe+mrWiUqF1sUFnYu+Se1QXlSIubISANX596n6uupfLj2mgg1ffMzx3TsA8AoM4tEPPrtY/hrR2Pu3JCxCCCEadPpgIov+9go6vYEnPv4Kvatbq7yPzWpl9YfvkrRtc6vU7wjuvn74hoYR2qMnHXr2JrRHT1yMrvWWPbJ5A6s/fPeq37Pn8FimPPMCao3mqutqK429f2ubUumcOXP46aefOHbsGEajkWHDhvHWW28RGRlZXaa0tJSXXnqJpUuXkpeXR0REBM8++yxPPdVwZj5v3jwefvjhOscrKiowGAxNCVEIIUQLCo/uh19YOHnpaRzeFMeAm6a3yvtotFpumv17fEM7cObIQVBAUZTzLzuc/5rqY7W/r/+4/Xw9dpTz/6KAggJ2OwrUOq++ukBBsStV51TXodSuT6l/enZpfh6l+XmkHdoPVPXgBEZ0JSyqNx2ietMhsheunl4AaF1cLts+KrX6YhJy/v3Pf1Nro8pjv8ZTXlTItN/9H0aP6+vDe5N6WCZPnszdd9/NoEGDsFqt/PnPf+bgwYMcOXIEN7eqrPvxxx9n48aNfPbZZ0RERLBu3TqefvppFi9ezPTp9f+iz5s3j+eee46kpKRax4ODgxt9IdLDIoQQrSNx3Sp++fwjvINDeOS9/51/dCIupdRIkkzlZRRknuXcmVNkHDtC+tHDFOdm1znHLyz8fAITjd7ois5gQKPVcTJxD4fjf6HkXC4AYx58/IrJYvLOX1nz4XtYTJV4BQVz64uv4B8e0RqX2qLa5JFQbm4ugYGBxMfHM2rUKACio6OZMWMGr776anW5gQMHctNNN/HGG2/UW8+8efP43e9+R2FhYXNDkYRFCCFaibmygk+eeghTeRm3v/Q6nfvf4OiQrknF53LJOHaY9KOHyDh2hLz0tDplvAKD6NCzN0c2b6h13ODmzjNfLLzie+SmnWLZv96gKCcbnd7A+MeeJmrkGKce19LY+/dVpclFRUUA+Pr6Vh8bMWIEy5cvJyMjA0VR2LhxI8nJyUyaNOmydZWWltKpUyfCwsKYNm0aCQkJly1vMpkoLi6u9RJCCNHyXAxGeo8eD7T8FOf2xNM/gKgRo5nw+Cweeucjnvr0W275/Z8YOHU6QV26oVKpKcrJrpOsAAya/ptGvUdAeAT3/f09wqP7YjFVsvrDd/n53//EXFHe0pfT5prdw6IoCtOnT6egoIAtW7ZUHzebzTz++OPMnz8frVaLWq3ms88+Y+bMmQ3WtWPHDo4fP06fPn0oLi7m3//+N6tWrWL//v1079693nNef/11/vKXv9Q5Lj0sQgjR8goyM/jid08A8Mi/P8EnONTBEV1/TOXlZCYfJf3YYU4m7CXn1AmgqnflyU++QaNt/LBTu83GrmWL2L7oO+w2G35h4Ux/8c/4hHRorfCbrdUfCT3zzDOsXLmSrVu3EhYWVn387bff5tNPP+Xtt9+mU6dObN68mZdffpklS5Ywfvz4RtVtt9sZMGAAo0aN4oMPPqi3jMlkwmS6uKRzcXExHTt2lIRFCCFayU9zXuNk4l4G3DSdMQ8+7uhwrntWs5nctJN4BQZXD85tqrPJx1jx7t8pLchH7+rGTc++SJf+g1o40qvTqgnL7NmzWbp0KZs3b6Zz587VxysqKvDy8mLJkiVMnXpxn4PHHnuM9PR01qxZ0+j3ePzxx0lPT2f16tWNKi9jWIQQonWdTNjDT/94HRejK098/BUuBqOjQxKNUFqQz4p353A2+SioVAy/634G33aX04xraZUxLIqiMGvWLH766Sc2bNhQK1kBsFgsWCwW1JeMINdoNNibsCunoigkJiYSEhLSlPCEEEK0ooh+A/AJCcVcUc6RzRsdHY5oJHcfX+567e/0mzAFFIVfv/+apf/8K2WFBY4OrUmalLA888wzfPPNN3z33Xd4eHiQlZVFVlYWFRUVAHh6ehIbG8sf/vAHNm3axMmTJ5k3bx7z58/ntttuq67ngQce4OWXX67+/i9/+Qtr164lNTWVxMREHn30URITE3nyySdb6DKFEEJcLZVaTczEqt7zxLU/c52sO9ouaLQ6xj/2DBN+OxuNVkvqvt3Me/EZknf+6ujQGq1JCcvcuXMpKipi9OjRhISEVL++//776jILFy5k0KBB3HffffTq1Yt//OMfvPnmm7WSj7S0NDIzM6u/Lyws5Le//S1RUVFMnDiRjIwMNm/ezI033tgClyiEEKKl9B49Hp3eQF56WvWCaOLa0XfcJO6b8z4BEV2oLClmxbtziPt8rqPDahRZml8IIUSTxH0+l/3rVtL1hiHc+odXHB2OaAKb1UJ5UREleedY+s+/UlFSjEan43ffLHFYTK2yNL8QQgjRf9I09q9bSereXRTlZOMVGOTokEQNFrOJvLTT5KadIvf0SfIyzlCan0dZQT6m8rI65bsOuDaeZkjCIoQQokn8wjoS3ieGtIOJJK5bSez9jzg6pHZNURT2/LyEzd980azzvUNC2bVsEUCt3aurd58+v0O0X1hHIvoNaJmgm0ESFiGEEE3Wf/LNpB1M5NCGdQy78150etmo1lFK8/OanawA7Fr6Y6PLPv7hl3j6BzT7va6GJCxCCCGarMuAG/AMCKI4N5ujW+PpO+7y26+I1uPu68fYh59gw5f/wyckFP+OEbh6+8CF3aYvDFW9sFN19W7PF3anvlBTjd2qoXrXaovJxIk9OwDQGRyXmErCIoQQosnUag0xk6ay+ZsvSFyzgj5jJzrNQmTtjUqlov/km+k/+eZWqT/9yCFO7NmBh38ARnePVnmPxpA9woUQQjRL9JgJaF305KadIuPYYUeHI1pJzulUAAIjujg0DklYhBBCNIvR3YOoEbEAJKz52cHRiJZWfC6H7YsXsHv5YsDxCYs8EhJCCNFs/SffzMEN60jZtY2SvHN4+Pk7OiRxFSxmE8d3buPQpjjSDh84Pw4G9G5udBs01KGxScIihBCi2QI6dSYsKpr0o4fYv341I+6e6eiQRDNknUjh4C9rObZtM+aK8urjHXv3JXr0eLrfOMyhA25BEhYhhBBXqf/kaaQfPcSBX9Yw5PYZaF1cHB2SaILknb+y4r1/VPemeAYE0Tt2HL1jxznVooCSsAghhLgq3QYNxd3Pn9K8cyRt30Lv2HGODkk0UnlRIXGffgiKQpeBNzLwplvp2Csaldr5hrg6X0RCCCGuKWqNhpgJNwFVg2+vky3q2oVfPp9LRUkxAeER3PLCy4RH93XKZAUkYRFCCNEC+oybhEanIzs1hcyUJEeHIxohafsWknf+ilqjYdLTz6PR6hwd0mVJwiKEEOKquXp60XPYKAAS1qxwcDTiSsqLCvnl87kA3HjrXQR17urgiK5MEhYhhBAt4sJKq8k7fqWssMDB0YiGKIpC3OcfVT0K6tSZIbff5eiQGkUSFiGEEC0iqEs3Qnr0xG6zsn/9akeHIxqQtH0LKTu3odZomHwNPAq6QBIWIYQQLab/pGkAHIhbjc1qcXA04lJlhQX88sXHAAy+7S6Hr17bFJKwCCGEaDE9hgzHzduHssICknduc3Q44hK/fD6XyvOPggbfdm08CrpAEhYhhBAtRqPV0Xf8ZAASZX8hp5KdepyUXdfeo6ALJGERQgjRovqOn4Jao+Fs8lGyU487Ohxx3sEN6wDoMWTENfUo6AJJWIQQQrQodx9fegwZAcguzs7CYjZx7Nd4AKJHT3BwNM0jCYsQQogW139y1eDbY9viKS8ucnA04vjObZjKy/AMCCQ8uq+jw2kWSViEEEK0uJDuPQnq0g2bxcLBX9Y6Opx279Cm9QD0jh3vtEvvX8m1GbUQQginplKpqheS279+NXabzcERtV+F2VmkHToAKhXRo8c7Opxmk4RFCCFEq4gcOhKjhyclebkc37PD0eG0W4fj4wDo1CcGz4BAB0fTfJKwCCGEaBVaF5fqKc6yv5Bj2O02Dm2qSliix1ybg20vkIRFCCFEq+k34SZUajXpRw6Re/qko8Npd04fSKQ07xwGN3e63TDE0eFcFUlYhBBCtBoPP3+6DxoKQMJameLc1g5trBpsGzVyDFoXFwdHc3UkYRFCCNGqYs5PcT66ZRMVpSWODaadKcjMALhmZwbVdO1fgRBCCKcWFhWNf3gEVrOp+hO/aBvDZ9wPwL7Vy8lIOurgaK6OJCxCCCFaVdUU56pelv3rVmK3yxTnttJ14GB6x44DRWHt3PewmCodHVKzScIihBCi1UWNGI3BzZ2inGxOJuxxdDjtyugHH8fd14+CzLNsXTDf0eE0myQsQgghWp1ObyB67ERA9hdqawY3dyY+8SxQ9WjozJGDDo6oeSRhEUII0SZiJt4EKhWnDySQl3HG0eG0K51jBtJn3CQA1s59H6vZ7OCImk4SFiGEEG3CKzCYrgNvBCBRpji3udj7H8Xd14+inGz2rV7u6HCaTBIWIYQQbab/pKr9hQ7Hb8BUXu7gaNoXvasrI+5+AICdS3645nbRloRFCCFEmwnv0w/fDh2xVFZU73Ej2k6vkWMIiOiCuaKcHYsXOjqcJpGERQghRJtRqVT0n1Q1xTlx7c8odruDI2pfVGo1o2c+CsD+9avIP5vh4IgaTxIWIYQQbapX7FhcjK4UZJ7l1IEER4fT7oRH96PLgEHYbTa2fDfP0eE0miQsQggh2pSLwUj06PGA7OLsKKPuexiVSs3x3dtJP3rI0eE0iiQsQggh2ly/iVMBOJm4l4Kssw6Opv3xCwunz7iqdXHiv/78mng0JwmLEEKINucb2oGImIGgKCSuXenocNqlYXfeh85gJOtECknbtzg6nCuShEUIIYRDXNhf6NDG9ZgrKxwcTfvj5u3DjbfcAcCWBV85/WJykrAIIYRwiM79BuIdHIK5opyjWzY6Opx2aeC0W3H38aU4N4cEJ1/MTxIWIYQQDqFSq4mZWNXLkrDmZxRFcXBE7Y9Ob2D4hcXkfvqeipJiB0fUMElYhBBCOEz0mPHo9Aby0tM4c/iAo8Npl3qNGkNAp86YysucejE5SViEEEI4jN7VjV6jxgIyxdlR1GoNsfdXLSaXuG4lBZnOuZicJCxCCCEc6sLg2xN7dlGcm+PgaNqnTn1j6BwzsGoxuQVfOTqceknCIoQQwqH8wsIJj+6HothJXCdTnNuCoihUlJaQnXqc5B1b2b18MahUAKTs3MbZ5GMOjrAuraMDEEIIIfpPvpm0Q/s5uGEdQ++8F52L3tEhXfMslZUU5WRRlJtDUU42xblZFOVkV7/MFQ3vll2UnUloj55tGO2VScIihBDC4boMHIRnQBDFudkc2xpPn7ETHR2S07NZLRSfy61KRnKyq5KTnGyKcqsSkorioivW4erljVdgEF6BwXgFBuEZEIRfh46ERka1wRU0jSQsQgghHE6t1hAz8SY2f/slCWtWED1mAqrzjyjaK7vdRml+flUycj4JKc692ENSmp+Holx+SX29mxteAeeTkcAgvAICayQngej0hja6mqsnCYsQQginED1mAtt++Jbc0yfJOHaYsKhoR4fUqhRFoaKk+GLPyIWektyq3pLi3FzsNutl69C66PEMCDzfSxKEV0BVb4nn+e8Nbu5tdDWtTxIWIYQQTsHo4UnPEaM5tHEdCWt+vi4SFlN5+flxJBce22TX+tpiqrzs+WqNBg//gPOJSI1k5Pz3rl7e7aYnShIWIYQQTqP/5Gkc2riOlF3bKMk7h4efv6NDAqoez5jKyqgsLaGytPT8vyVUlpXW+b6itITMxs6yUalw9/HFszohCaqVnLj7+qHWaFr34q4RkrAIIYRwGoERXejQszcZxw5zIG41w2fMbNH6rRYLlaUlmM4nFrWSjfoSkbKS8+XLmv2eBg/PGknIxaTEMzAYz4BAtDpdC17h9UsSFiGEEE6l/+SbqxKWX9Yy+Pa769zQFUXBYqqsm2SUViUhprLSOj+rOH/MajJdVWwuRiMGdw8Mbh4Y3N2rvnZ3x+B24euq7ytLS9HqdHSI6o2nf+BVvaeoIgmLEEIIp9Jt0BDcff0ozc9j6T//ikarvZh8nO/5uNJg1MtRqdTo3d0xurtXJx76GgmH0b128lGVoFSV0Wjltuko0vJCCCGcikarJWbiVLYunM/pAwmXLWfw8Dzfu+FeT89H/b0feqMrKrUs9H6tkYRFCCGE0xk47TY0Oh12m+18r0fdRETrom83M2SEJCxCCCGckFan44Zptzk6DOFEpE9MCCGEEE5PEhYhhBBCOD1JWIQQQgjh9CRhEUIIIYTTk4RFCCGEEE6vSQnLnDlzGDRoEB4eHgQGBnLrrbeSlJRUq0xpaSmzZs0iLCwMo9FIVFQUc+fOvWLdixcvplevXuj1enr16sWSJUuadiVCCCGEuG41KWGJj4/nmWeeYceOHaxfvx6r1crEiRMpq7HHwvPPP8+aNWv45ptvOHr0KM8//zyzZ89m2bJlDda7fft2ZsyYwcyZM9m/fz8zZ87krrvuYufOnc2/MiGEEEJcN1SKoijNPTk3N5fAwEDi4+MZNWoUANHR0cyYMYNXX321utzAgQO56aabeOONN+qtZ8aMGRQXF7N69erqY5MnT8bHx4cFCxY0Kpbi4mK8vLwoKirC09OzuZckhBBCiDbU2Pv3VY1hKSoqAsDX17f62IgRI1i+fDkZGRkoisLGjRtJTk5m0qRJDdazfft2Jk6cWOvYpEmT2LZtW4PnmEwmiouLa72EEEIIcX1qdsKiKAovvPACI0aMIDo6uvr4Bx98QK9evQgLC8PFxYXJkyfz0UcfMWLEiAbrysrKIigoqNaxoKAgsrKyGjxnzpw5eHl5Vb86duzY3EsRQgghhJNrdsIya9YsDhw4UOeRzQcffMCOHTtYvnw5e/fu5Z133uHpp58mLi7usvVduh+EoiiX3SPi5ZdfpqioqPp15syZ5l6KEEIIIZxcs/YSmj17NsuXL2fz5s2EhYVVH6+oqOBPf/oTS5YsYerUqQD07duXxMRE3n77bcaPH19vfcHBwXV6U3Jycur0utSk1+vR6/XNCV8IIYQQ15gm9bAoisKsWbP46aef2LBhA507d671c4vFgsViQX3Jtt0ajQa73d5gvUOHDmX9+vW1jq1bt45hw4Y1JTwhhBBCXKea1MPyzDPP8N1337Fs2TI8PDyqe0W8vLwwGo14enoSGxvLH/7wB4xGI506dSI+Pp758+fz7rvvVtfzwAMP0KFDB+bMmQPAc889x6hRo3jrrbeYPn06y5YtIy4ujq1btzY6tguTnWTwrRBCCHHtuHDfvuKkZaUJgHpfX375ZXWZzMxM5aGHHlJCQ0MVg8GgREZGKu+8845it9ury8TGxioPPvhgrbp//PFHJTIyUtHpdErPnj2VxYsXNyU05cyZMw3GJy95yUte8pKXvJz7debMmcve569qHRZnYrfbOXv2LB4eHpcdrOtoxcXFdOzYkTNnzsh6MU0g7dY80m7NJ23XPNJuzdOe201RFEpKSggNDa0zpKSmZg26dUZqtbrWAGBn5+np2e5+KVuCtFvzSLs1n7Rd80i7NU97bTcvL68rlpHND4UQQgjh9CRhEUIIIYTTk4Sljen1el577TVZQ6aJpN2aR9qt+aTtmkfarXmk3a7suhl0K4QQQojrl/SwCCGEEMLpScIihBBCCKcnCYsQQgghnJ4kLEIIIYRwepKwtLDk5GSmT5+Ov78/np6eDB8+nI0bN9Yq89xzzzFw4ED0ej0xMTGNrnv79u2MHTsWNzc3vL29GT16NBUVFS18BY7Rmu0GVSspTpkyBZVKxdKlS1sucCfQGm2Xn5/P7NmziYyMxNXVlfDwcJ599lmKiopa6SraXmv9zplMJmbPno2/vz9ubm7ccsstpKent8IVOEZj2i0tLY2bb74ZNzc3/P39efbZZzGbzZetNysri5kzZxIcHIybmxsDBgxg0aJFrXkpbaq12g2u73tDTZKwtLCpU6ditVrZsGEDe/fuJSYmhmnTplVvFAlVN89HHnmEGTNmNLre7du3M3nyZCZOnMiuXbvYvXs3s2bNuuwyxteS1mq3C95//32n3rLharRG2509e5azZ8/y9ttvc/DgQebNm8eaNWt49NFHW+sy2lxr/c797ne/Y8mSJSxcuJCtW7dSWlrKtGnTsNlsrXEZbe5K7Waz2Zg6dSplZWVs3bqVhQsXsnjxYn7/+99ftt6ZM2eSlJTE8uXLOXjwILfffjszZswgISGhLS6r1bVWu13v94ZamrTDoLis3NxcBVA2b95cfay4uFgBlLi4uDrlX3vtNaVfv36Nqnvw4MHKK6+80lKhOpXWbDdFUZTExEQlLCxMyczMVABlyZIlLRC1c2jttqvphx9+UFxcXBSLxdLccJ1Ga7VbYWGhotPplIULF1Yfy8jIUNRqtbJmzZoWid2RGtNuq1atUtRqtZKRkVFdZsGCBYper1eKiooarNvNzU2ZP39+rWO+vr7KZ5991sJX0fZas92u53vDpa7DFMxx/Pz8iIqKYv78+ZSVlWG1Wvnf//5HUFAQAwcObHa9OTk57Ny5k8DAQIYNG0ZQUBCxsbFs3bq1BaN3nNZqN4Dy8nLuuece/vvf/xIcHNxCETuP1my7SxUVFeHp6YlWe+1vQdZa7bZ3714sFgsTJ06sPhYaGkp0dDTbtm1ridAdqjHttn37dqKjowkNDa0+b9KkSZhMJvbu3dtg3SNGjOD7778nPz8fu93OwoULMZlMjB49urUvq9W1Vrtd7/eGS137//M4EZVKxfr165k+fToeHh6o1WqCgoJYs2YN3t7eza43NTUVgNdff523336bmJgY5s+fz7hx4zh06BDdu3dvoStwjNZqN4Dnn3+eYcOGMX369JYJ1sm0ZtvVlJeXxxtvvMETTzzRYnU6Umu1W1ZWFi4uLvj4+NQ6HhQUVOtR07WqMe2WlZVFUFBQrfN8fHxwcXG5bBt8//33zJgxAz8/P7RaLa6urixZsoSuXbu25iW1idZqt+v93nAp6WFphNdffx2VSnXZ1549e1AUhaeffprAwEC2bNnCrl27mD59OtOmTSMzM7PZ72+32wF44oknePjhh+nfvz/vvfcekZGRfPHFFy11mS3O0e22fPlyNmzYwPvvv99yF9VGHN12NRUXFzN16lR69erFa6+91iJ1thZnareaFEVx6jFULd1u9V3rldrglVdeoaCggLi4OPbs2cMLL7zAnXfeycGDB1vlmluCo9vtWr03NJf0sDTCrFmzuPvuuy9bJiIigg0bNvDzzz9TUFBQvT34Rx99xPr16/nqq6946aWXmvX+ISEhAPTq1avW8aioKNLS0ppVZ1twdLtt2LCBEydO1PnEfMcddzBy5Eg2bdrUrHrbgqPb7oKSkhImT56Mu7s7S5YsQafTXVV9rc3R7RYcHIzZbKagoKBWL0tOTg7Dhg1rVp1toSXbLTg4mJ07d9Y6t6CgAIvFUqcH4YITJ07w3//+l0OHDtG7d28A+vXrx5YtW/jwww/5+OOPW+AqW56j2+1avTc0lyQsjeDv74+/v/8Vy5WXlwPUGZ2tVqurM+HmiIiIIDQ0lKSkpFrHk5OTmTJlSrPrbW2ObreXXnqJxx57rNaxPn368N5773HzzTc3u9624Oi2g6qelUmTJqHX61m+fDkGg+Gq6msLjm63gQMHotPpWL9+PXfddRcAmZmZHDp0iH/+85/Nrre1tWS7DR06lDfffJPMzMzqG+q6devQ6/UNjg9qqF6NRnPVv8etydHtdq3eG5rNYcN9r0O5ubmKn5+fcvvttyuJiYlKUlKS8uKLLyo6nU5JTEysLpeSkqIkJCQoTzzxhNKjRw8lISFBSUhIUEwmk6IoipKenq5ERkYqO3furD7nvffeUzw9PZUff/xRSUlJUV555RXFYDAox48fb/PrbGmt2W6X4jqcJdQabVdcXKwMHjxY6dOnj3L8+HElMzOz+mW1Wh1yrS2pNX/nnnzySSUsLEyJi4tT9u3bp4wdO1bp169fu2k3q9WqREdHK+PGjVP27dunxMXFKWFhYcqsWbOq67m03cxms9KtWzdl5MiRys6dO5Xjx48rb7/9tqJSqZSVK1c65FpbUmu1m6Jc3/eGS0nC0sJ2796tTJw4UfH19VU8PDyUIUOGKKtWrapVJjY2VgHqvE6ePKkoiqKcPHlSAZSNGzfWOm/OnDlKWFiY4urqqgwdOlTZsmVLG11V62vNdqvpektYFKV12m7jxo31lq95zrWutX7nKioqlFmzZim+vr6K0WhUpk2bpqSlpbXhlbWuxrTb6dOnlalTpypGo1Hx9fVVZs2apVRWVlb/vL52S05OVm6//XYlMDBQcXV1Vfr27VtnmvO1rLXaTVGu73tDTSpFUZTW7sURQgghhLgaMktICCGEEE5PEhYhhBBCOD1JWIQQQgjh9CRhEUIIIYTTk4RFCCGEEE5PEhYhhBBCOD1JWIQQQgjh9CRhEUIIIYTTk4RFCCGEEE5PEhYhhBBCOD1JWIQQQgjh9CRhEUIIIYTT+//bbjM8BaHhugAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1966 True False t,unbroken, noEnclave\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2027 True False t,unbroken, noEnclave\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3467 True False t,unbroken, noEnclave\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4361 True False t,unbroken, noEnclave\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4654 True False t,unbroken, noEnclave\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#appear to have a handful of residual minor enclaves.  Mop them up if possible\n",
    "maxLoop = 5\n",
    "for t in enclaveOnly:   \n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(t,unbroken,noEnclave,\"t,unbroken, noEnclave\")\n",
    "        for u in HDunitList[t]:\n",
    "            plotPoly(unitGeom[u],0.2)\n",
    "        eLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nowNoEnclave = False\n",
    "        if len(eLists) > 0:\n",
    "            totEpop = 0.\n",
    "            for i,L in enumerate(eLists):\n",
    "                for uu in L:\n",
    "                    totEpop += unitPop[uu]\n",
    "                    plotPoly(unitGeom[uu])\n",
    "                    plotCenter(i,unitGeom[uu])\n",
    "            if totEpop <= 0.05 * aDP:\n",
    "                for i, L in enumerate(eLists):\n",
    "                    for uu in L:\n",
    "                        HDunitList[t].append(uu)\n",
    "                nowNoEnclave, enclaveList2 = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "        if nowNoEnclave:\n",
    "            plotCenter(\"fixed\",hdCP[t])\n",
    "        else:\n",
    "            plotCenter(\"still broken\",hdCP[t])\n",
    "        plotPoly(HDpoly[t].intersection(MAP),1.5)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "f3bcd6a8-40b6-4764-a0d1-6814e3286cb8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking on our corner clusters and unit county usage\n",
      "usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\n",
      "2.11403     0     12126 0.5\n",
      "0.85142     1     14004 0.5\n",
      "0.90382     2     8226 0.5\n",
      "0.84592     3     2991 0.5\n",
      "0.97339     6793     82874 0.25\n",
      "0.996     6794     90352 0.25\n"
     ]
    }
   ],
   "source": [
    "print(\"checking on our corner clusters and unit county usage\")\n",
    "print(\"usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\")\n",
    "for u in range(nUnits):\n",
    "    if int(allUnits[u]) != allUnits[u] :\n",
    "        print(r5(unitUse[u]),\"   \",u,\"   \",int(unitPop[u]), allUnits[u]%1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "8e419d31-b530-4ff5-9cb2-5ba8265cc5f8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final check -- any more disco's ?\n",
      "working on HD 8 time is now 0.0 sec\n",
      "working on HD 778 time is now 3.5886363983154297 sec\n",
      "working on HD 1560 time is now 7.4492270946502686 sec\n",
      "working on HD 2385 time is now 10.55156683921814 sec\n",
      "working on HD 2899 time is now 13.810655117034912 sec\n",
      "working on HD 3435 time is now 16.325815677642822 sec\n",
      "working on HD 3968 time is now 19.677818775177002 sec\n",
      "working on HD 4475 time is now 22.727598428726196 sec\n",
      "working on HD 5004 time is now 25.416892528533936 sec\n",
      "working on HD 5525 time is now 29.701643466949463 sec\n",
      "working on HD 6071 time is now 33.33163070678711 sec\n",
      "working on HD 6686 time is now 39.49396061897278 sec\n",
      "out of 5979 total HDs, there were 5979 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 45 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",time.time() - startTime,\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "1e983c03-777a-4e81-bed0-34a620175f83",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's visualize over-used and underused units, < 0.75 or > 1.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here is the histogram of HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "minUnitUse, maxUnitUse = 0.75, 1.25\n",
    "print(\"let's visualize over-used and underused units, <\",minUnitUse,\"or >\",maxUnitUse)\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minUnitUse:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(\"u\",unitCP[u])\n",
    "    if unitUse[u] > maxUnitUse:\n",
    "        plotPoly(unitGeom[u], 1.5)\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"and here is the histogram of HD pops\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins = [0, 0.6*aDP, 0.7*aDP, 0.85*aDP, 0.9*aDP, 0.95*aDP,\n",
    "         0.98*aDP, aDP, 1.02*aDP, 1.05*aDP, 1.1*aDP, 1.15*aDP, 1.3*aDP, 1.5*aDP, 2.0*aDP])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "114705bb-9640-45f1-9f25-030030efaa63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#below here -- working on tightening use distro while squaring up pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "5d7d8519-eb3d-4345-9ac6-ab888e61c348",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 0.92672 0.13773\n"
     ]
    }
   ],
   "source": [
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        latestHDpop[t] += unitPop[u]\n",
    "        latestUnitUse[u] += nDistricts * HDweight[t]\n",
    "latestUnitWeights, latestUnitDistro = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "17caead2-8aba-4fc5-817e-53d440c5a704",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"ContigButOffPopUnit.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "c5d76a90-4441-4e96-b9a7-7de433686812",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving pre-squaring HD unit lists to file\n"
     ]
    }
   ],
   "source": [
    "print(\"saving pre-squaring HD unit lists to file\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        for i,uu in enumerate(surrounders):\n",
    "            if u == uu:\n",
    "                unitVTDlist[u].append(surroundedVTDs[i])\n",
    "\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        vtdList[t] += unitVTDlist[u]\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"contigNotSquared.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "b1971853-bda7-49a4-9f25-1bdfd5e75aa2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 8\n",
      "working on avg unit-to-HDcp dist for HD 1350\n",
      "working on avg unit-to-HDcp dist for HD 2488\n",
      "working on avg unit-to-HDcp dist for HD 3322\n",
      "working on avg unit-to-HDcp dist for HD 4173\n",
      "working on avg unit-to-HDcp dist for HD 5004\n",
      "working on avg unit-to-HDcp dist for HD 5827\n",
      "working on avg unit-to-HDcp dist for HD 6795\n"
     ]
    }
   ],
   "source": [
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "f179943a-ef19-4036-a8c6-5ab7b1f97bf8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Restart\n",
    "HDunitList = [latestHDlist[t].copy() for t in range(nHDs)]\n",
    "unitUse = [0. for u in range(nUnits) ]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, weights = unitPop,bins = 50)\n",
    "plt.show()\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "27b51cd0-8bb6-4b16-bb79-37498e59ce5c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now square up the 5469 HDs with pop < 761670 to within 2239\n",
      "working on squaring up HD 8 time is now 0\n",
      "working on squaring up HD 197 time is now 119\n",
      "working on squaring up HD 325 time is now 716\n",
      "working on squaring up HD 603 time is now 804\n",
      "working on squaring up HD 739 time is now 883\n",
      "working on squaring up HD 839 time is now 1055\n",
      "working on squaring up HD 1212 time is now 1213\n",
      "working on squaring up HD 1312 time is now 1286\n",
      "working on squaring up HD 1422 time is now 1393\n",
      "working on squaring up HD 1849 time is now 1662\n",
      "working on squaring up HD 1982 time is now 1705\n",
      "working on squaring up HD 2157 time is now 1970\n",
      "working on squaring up HD 2287 time is now 2610\n",
      "working on squaring up HD 2391 time is now 2880\n",
      "working on squaring up HD 2494 time is now 3107\n",
      "working on squaring up HD 2600 time is now 3542\n",
      "working on squaring up HD 2707 time is now 3593\n",
      "working on squaring up HD 2812 time is now 3619\n",
      "working on squaring up HD 2917 time is now 3645\n",
      "working on squaring up HD 3019 time is now 3654\n",
      "working on squaring up HD 3127 time is now 3663\n",
      "working on squaring up HD 3233 time is now 3666\n",
      "working on squaring up HD 3352 time is now 4144\n",
      "working on squaring up HD 3476 time is now 4469\n",
      "working on squaring up HD 3591 time is now 4678\n",
      "working on squaring up HD 3696 time is now 4832\n",
      "working on squaring up HD 3822 time is now 4964\n",
      "working on squaring up HD 3935 time is now 5059\n",
      "working on squaring up HD 4056 time is now 5110\n",
      "working on squaring up HD 4167 time is now 5192\n",
      "working on squaring up HD 4268 time is now 5334\n",
      "working on squaring up HD 4399 time is now 5368\n",
      "working on squaring up HD 4506 time is now 5372\n",
      "working on squaring up HD 4628 time is now 5386\n",
      "working on squaring up HD 4754 time is now 5399\n",
      "working on squaring up HD 4879 time is now 5422\n",
      "working on squaring up HD 5004 time is now 5433\n",
      "working on squaring up HD 5134 time is now 5554\n",
      "working on squaring up HD 5247 time is now 5856\n",
      "working on squaring up HD 5350 time is now 5976\n",
      "working on squaring up HD 5452 time is now 6046\n",
      "working on squaring up HD 5552 time is now 6102\n",
      "working on squaring up HD 5654 time is now 6256\n",
      "working on squaring up HD 5757 time is now 6377\n",
      "working on squaring up HD 5864 time is now 6883\n",
      "working on squaring up HD 5984 time is now 6984\n",
      "working on squaring up HD 6127 time is now 7114\n",
      "working on squaring up HD 6248 time is now 7458\n",
      "working on squaring up HD 6370 time is now 7539\n",
      "working on squaring up HD 6521 time is now 8421\n",
      "working on squaring up HD 6640 time is now 8560\n",
      "working on squaring up HD 6763 time is now 8979\n",
      "working on squaring up HD 6886 time is now 9328\n",
      "working on squaring up HD 7002 time is now 9464\n",
      "working on squaring up HD 7119 time is now 9514\n",
      "We have addressed underpop in a total of 5469 HDs\n",
      "Here is a scatterplot of original (x) to final pop (y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING UP UNDERPOPPED\n",
    "HDnAddedUnits, HDaddedPop = [0]*nHDs, [0.]*nHDs\n",
    "underPoppedList = list()\n",
    "minDistrictPop, maxDistrictPop = 0.99 * aDP, 1.01 * aDP\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop < minDistrictPop and t in popHDlist:\n",
    "        underPoppedList.append(t)\n",
    "print(\"We will now square up the\",len(underPoppedList),\"HDs with pop <\",int(minDistrictPop),\"to within\",int(maxGap) )\n",
    "startTime = time.time()\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "for ii,t in enumerate(underPoppedList):\n",
    "    if ii%100 == 0:\n",
    "        print(\"working on squaring up HD\",t,\"time is now\",int(time.time() - startTime)) \n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "print(\"We have addressed underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original (x) to final pop (y)\")\n",
    "plt.scatter([HDvPop[t] - HDaddedPop[t] for t in underPoppedList], [HDvPop[t] for t in underPoppedList])\n",
    "plt.axhline(y=aDP, xmin = 0.9*aDP, xmax = 1.1*aDP, ls=\"--\")\n",
    "plt.axvline(x=aDP, ymin = 0.9*aDP, ymax = 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "bc01409d-d9e4-43a6-9fae-c76aa7bdc855",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous unit avg and sd usage are 0.92672 0.13773\n",
      "amped unit avg and sd usage are 0.99992 0.12345\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prevUnitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        prevUnitUse[u] += HDweight[t] * nDistricts\n",
    "\n",
    "ampedUnitUse = [0. for u in range(nUnits)]\n",
    "ampedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "ampedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in ampedHDlist[t]:\n",
    "        ampedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "plt.hist(prevUnitUse,bins=50,weights=unitPop,label=\"previous\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.legend()\n",
    "ampedUnitUseAvg, ampedUnitUseSD = getWeightedAvgAndSD(ampedUnitUse,unitPop)\n",
    "print(\"previous unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "46dc9be4-4cd6-4d82-ba39-aa4af6ad4bb1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CONSIDER MAKING A METHOD OUT OF THE PATCH-BUILD ROUTINE -- WHAT DO WE NEED TO PASS?\n",
    "plt.hist([ampedHDpop[t] for t in popHDlist],bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "dd7d9818-344c-4150-917b-57a4ee051c92",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\n",
      "Let's now square down the 83 overPopped HDs to within 2239\n",
      "working on squaring down HD 9\n",
      "working on squaring down HD 1890\n",
      "working on squaring down HD 4394\n",
      "We have addressed underpop in a total of 5469 HDs\n",
      "We have addressed overpop in a total of 83 HDs\n",
      "Here is a scatterplot of original to final pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING DOWN\n",
    "print(\"Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\")\n",
    "HDunitList = [ampedHDlist[t].copy() for t in range(nHDs)]\n",
    "HDvPop =     [ampedHDpop[t]         for t in range(nHDs)]\n",
    "unitUse =    [ampedUnitUse[u]       for u in range(nUnits)] #saving in case I need to re-run\n",
    "HDnShedUnits = [0]*nHDs\n",
    "overPoppedList = list()\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop > maxDistrictPop and t in popHDlist:\n",
    "        overPoppedList.append(t)\n",
    "\n",
    "maxGap = 0.9 * np.median(unitPop)   \n",
    "print(\"Let's now square down the\",len(overPoppedList),\"overPopped HDs to within\", int(maxGap))   \n",
    "for ii,t in enumerate(overPoppedList):\n",
    "    if ii%30 == 0:\n",
    "        print(\"working on squaring down HD\",t)\n",
    "    #avgDist = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "    excess = HDvPop[t] - aDP\n",
    "    origExcess = excess\n",
    "    HDboundaryList, HDboundaryScore = list(), list()  #these will be dynamic lists of the overused border units to jettison\n",
    "    starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "    barredSet = set(get2nbrs([starterU],unitNbrs)).union(barredJettisonSet)\n",
    "\n",
    "    for u in set(HDunitList[t]).difference(barredSet):\n",
    "        isBoundary = False\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t]:\n",
    "                isBoundary = True\n",
    "                break\n",
    "        if isBoundary and HDvPop[t] - unitPop[u] > aDP - maxGap:  #shedding this unit won't send us too far under targetpop\n",
    "            HDboundaryList.append(u)\n",
    "            HDboundaryScore.append((1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t])  #low-score = bias toward far, overused\n",
    "    currList = HDunitList[t].copy()\n",
    "    shedList, stillGoing = list(), True\n",
    "    while excess > maxGap and len(HDboundaryList) > 0:   #shed the highest-scoring neighboring overused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(HDboundaryScore), 0, True\n",
    "        while i < len(HDboundaryScore) and notYetPicked:        \n",
    "            listNo = idx[i]   #low (large negative) score is preferred to shed\n",
    "            unitNoToShed = HDboundaryList[listNo]  # attempt to shed this unit ...\n",
    "            tryList = list(set(currList).difference( {unitNoToShed} ) )\n",
    "            unbroken, noEnclave, sPL, ePL = enclaveCheck(tryList,unitNbrs) \n",
    "            if unbroken and noEnclave:             #... if that won't eff up contiguity\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #add this eligible unit\n",
    "            shedList.append(unitNoToShed)\n",
    "            excess -= unitPop[unitNoToShed]        \n",
    "            del currList[currList.index(unitNoToShed) ]\n",
    "            del HDboundaryList[listNo]\n",
    "            del HDboundaryScore[listNo]\n",
    "            for u in unitNbrs[unitNoToShed]:      # ... and ID any neighboring units that will now be on boundary after we shed this unit\n",
    "                if u in currList and u not in HDboundaryList and (unitPop[u] <= excess + maxGap and\n",
    "                                                                  u not in barredSet): \n",
    "                    HDboundaryList.append(u)\n",
    "                    HDboundaryScore.append( (1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t] )  #low-score, bias toward far & overused       \n",
    "            for uu in HDboundaryList.copy():\n",
    "                if unitPop[uu] > excess + maxGap:  #checking to see if the latest pop change DQ's any large units on current boundary\n",
    "                    del HDboundaryScore[HDboundaryList.index(uu)]\n",
    "                    del HDboundaryList[HDboundaryList.index(uu)]                \n",
    "    for u in shedList:\n",
    "        unitUse[u] -= HDweight[t] * nDistricts\n",
    "    HDunitList[t], HDvPop[t] = currList.copy(), np.sum( [unitPop[u] for u in currList ] )    \n",
    "    if HDvPop[t] < minDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has undershot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after shedding\",\n",
    "             np.sum([unitPop[u] for u in shedList]) )\n",
    "    \n",
    "    HDnShedUnits[t] = -1 * len(shedList)\n",
    "print(\"We have addressed underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "\n",
    "print(\"We have addressed overpop in a total of\",len(overPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original to final pop\")\n",
    "plt.scatter([ampedHDpop[t] for t in overPoppedList], [HDvPop[t] for t in overPoppedList])\n",
    "plt.axhline(aDP, 0.9*aDP, 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "9a19b23e-43cc-4609-9ee7-6d1057a7a9e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the stats prior to any equi-pop patching\n",
      "orig unit avg and sd usage are 0.92672 0.13773\n",
      "amped unit avg and sd usage are 0.99992 0.12345\n",
      "shed unit avg and sd usage are 0.99944 0.12336\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here are the final populations\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 8 out of 7211\n",
      "working on HD 445 out of 7211\n",
      "working on HD 881 out of 7211\n",
      "working on HD 1458 out of 7211\n",
      "working on HD 2005 out of 7211\n",
      "working on HD 2385 out of 7211\n",
      "working on HD 2693 out of 7211\n",
      "working on HD 3001 out of 7211\n",
      "working on HD 3322 out of 7211\n",
      "working on HD 3647 out of 7211\n",
      "working on HD 3968 out of 7211\n",
      "working on HD 4274 out of 7211\n",
      "working on HD 4577 out of 7211\n",
      "working on HD 4884 out of 7211\n",
      "working on HD 5223 out of 7211\n",
      "working on HD 5525 out of 7211\n",
      "working on HD 5827 out of 7211\n",
      "working on HD 6207 out of 7211\n",
      "working on HD 6584 out of 7211\n",
      "working on HD 6906 out of 7211\n",
      "all done checking HD and complement contiguity for all 7211 HDs\n",
      "underpop contigFail, enclaveFail = 0 0 out of 5469\n",
      "overpop contigFail, enclaveFail = 0 0 out of 83\n",
      "total contigFail, enclaveFail =  0 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Here are the stats prior to any equi-pop patching\")\n",
    "shedUnitUse = [0. for u in range(nUnits)]\n",
    "shedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "shedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in shedHDlist[t]:\n",
    "        shedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "plt.hist(latestUnitUse,bins=50,weights=unitPop,label=\"pre-squaring\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.hist(shedUnitUse, bins=50, weights=unitPop,label=\"shed\",histtype=\"step\")\n",
    "plt.legend()\n",
    "shedUnitUseAvg, shedUnitUseSD = getWeightedAvgAndSD(shedUnitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "print(\"shed unit avg and sd usage are\", r5(shedUnitUseAvg),  r5(shedUnitUseSD) )\n",
    "plt.show()\n",
    "# note: when I ran this early Jan, went from 0.12662 to 0.09961 to 0.09432 SD orig-amped-shed, but contig not yet done\n",
    "print(\"and here are the final populations\")\n",
    "plt.hist([shedHDpop[t] for t in popHDlist],bins=20)\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "failEnclaveSet, failContigSet = set(), set()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs,5)\n",
    "    if not noEnclave:\n",
    "        noEnclave, eList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not unbroken or not noEnclave:\n",
    "        #print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "        pass\n",
    "    if not unbroken:\n",
    "        failContigSet.add(t)\n",
    "    if not noEnclave:\n",
    "        failEnclaveSet.add(t)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")\n",
    "failContigUnderpopped, failEnclaveUnderpopped = set(underPoppedList).intersection(failContigSet), set(underPoppedList).intersection(failEnclaveSet)\n",
    "failContigOverpopped, failEnclaveOverpopped = set(overPoppedList).intersection(failContigSet), set(overPoppedList).intersection(failEnclaveSet)\n",
    "print(\"underpop contigFail, enclaveFail =\",len(failContigUnderpopped), len(failEnclaveUnderpopped),\"out of\",len(underPoppedList))\n",
    "print(\"overpop contigFail, enclaveFail =\",len(failContigOverpopped), len(failEnclaveOverpopped),\"out of\",len(overPoppedList))\n",
    "print(\"total contigFail, enclaveFail = \",len(failContigSet), len(failEnclaveSet) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "e7bfa674-8fc8-42c8-86e9-76d334d3c47c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"unpatchedContigGoodPop.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "c0c3a3d8-cf79-40ad-aa2e-b6793266f1d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    }
   ],
   "source": [
    "print(\"prior to patching, write these contiguous lists to a file, converting to vtd lists\")  #not yet done for FL; split VTDs\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        for i,uu in enumerate(surrounders):\n",
    "            if u == uu:\n",
    "                unitVTDlist[u].append(surroundedVTDs[i])\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        vtdList[t] += unitVTDlist[u]\n",
    "    #add more stuff here to convert to vtd lists\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"needsEnclaveRemoval.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1b794580-5a58-4238-b310-7c87872ed679",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\n",
      "Must have already established unitGeoms, pops, topology\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched UNIT list, e.g. ./2024state_HD_output/FL7211unpatchedContigGoodPop.csv ./2024state_HD_output/FL7211unpatchedContigGoodPop.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in 7211 HD lists of units\n",
      "orig unit avg and sd usage are 0.99944 0.12336\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\") #vtdlist for patching\")\n",
    "print(\"Must have already established unitGeoms, pops, topology\")\n",
    "infile = input(\"enter the unpatched UNIT list, e.g. ./2024state_HD_output/FL7211unpatchedContigGoodPop.csv\")\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDunitList\"]  #inDF[\"HDvtdList\"]\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of units\") #vtds\")\n",
    "HDunitList = [inVTDlist[t].copy() for t in range(nHDs)]\n",
    "#HDunitList = [list() for t in range(nHDs)]\n",
    "#for t in range(nHDs):\n",
    "#    if t%500 == 0:\n",
    "#        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "#    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "#        if v in allUnits:\n",
    "#            HDunitList[t].append(allUnits.index(v))\n",
    "#    for c in unitCounties:\n",
    "#        if countyTractList[c][0] in inVTDlist[t]:\n",
    "#            u = allUnits.index(c+0.5)\n",
    "#            HDunitList[t].append(u)\n",
    "#    for j, L in enumerate(CCBlist):\n",
    "#        c = L[0]\n",
    "#        if countyTractList[c][0] in inVTDlist[t]:\n",
    "#            u = allUnits.index(j+0.25)\n",
    "#            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5f3a5392-11f1-4061-a458-aecd834b2748",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on HD 0\n",
      "working on HD 500\n",
      "working on HD 1000\n",
      "working on HD 1500\n",
      "working on HD 2000\n",
      "working on HD 2500\n",
      "working on HD 3000\n",
      "working on HD 3500\n",
      "working on HD 4000\n",
      "working on HD 4500\n",
      "working on HD 5000\n",
      "working on HD 5500\n",
      "working on HD 6000\n",
      "working on HD 6500\n",
      "working on HD 7000\n",
      "all avgDist to HD centers computed\n"
     ]
    }
   ],
   "source": [
    "#needed for restart only  about 5sec per 2000 HDs\n",
    "avgDist = [0. for t in range(nHDs)]\n",
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if t%800 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    if HDvPop[t] > 0.1 * aDP:\n",
    "        avgDist[t] = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        popHDlist.append(t)\n",
    "print(\"all avgDist to HD centers computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ea0db02f-9df8-4906-acb4-086c29c0fcc1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on HD 300\n",
      "working on HD 600\n",
      "working on HD 900\n",
      "working on HD 1200\n",
      "working on HD 1500\n",
      "out of 1652 total HDs, there were 1652 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n"
     ]
    }
   ],
   "source": [
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()  #optional contiguity check\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "27b42f93-dc87-4afe-8eb3-f30fdff4a737",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking unitUse for county clusters\n",
      "6793 0.9733880685005182 82874.0\n",
      "6794 0.9960002441574092 90352.0\n"
     ]
    }
   ],
   "source": [
    "print(\"checking unitUse for county clusters\")\n",
    "for uNo in allUnits:\n",
    "    if uNo - int(uNo) > 0.23 and uNo - int(uNo) < 0.27:\n",
    "        u = allUnits.index(uNo)\n",
    "        print(u,unitUse[u], unitPop[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "72941b8f-7be6-4a31-9377-618f44a34336",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unpatchedUnitUse = unitUse.copy()              #... for safekeeping\n",
    "unpatchedHDlist =  [HDunitList[t].copy() for t in range(nHDs) ]\n",
    "unpatchedHDpop =   HDvPop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5f9ca091-1eb6-4135-a233-7f5ae0f5e3d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unitUse = unpatchedUnitUse.copy()              #... for restarting\n",
    "HDunitList =  [unpatchedHDlist[t].copy() for t in range(nHDs) ]\n",
    "HDvPop =   unpatchedHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e9dd457d-fb37-49cf-9b19-19c17c328ede",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we exchange in under-used, THEN shed over-used\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.05\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will stop patching on individual underused units when their usage exceeds 0.95\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMinUse value 0.95\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.03\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's further tighten the distro with exchanges up to 23080\n",
      "current avg and SD of unit usage are 0.99943 0.07697 . Now trying to increase up to 20 units' underusage\n",
      "starting to increase usage for unit 4636 with usage 0.71631 . Total UU units tried = 1\n",
      "try to add unit 4636 from 0 th HD.  Usage up to 0.7163056851481955\n",
      "try to add unit 4636 from 20 th HD.  Usage up to 0.7863180345049499\n",
      "try to add unit 4636 from 40 th HD.  Usage up to 0.8413725797886029\n",
      "try to add unit 4636 from 60 th HD.  Usage up to 0.9231777824956633\n",
      "all done trying to reduce usage of unit 4636 final usage = 0.95517 50 sec elapsed 47 2 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.99945 0.07628\n",
      "starting to increase usage for unit 1657 with usage 0.72115 . Total UU units tried = 2\n",
      "try to add unit 1657 from 0 th HD.  Usage up to 0.7211460451037149\n",
      "try to add unit 1657 from 20 th HD.  Usage up to 0.7788651279568755\n",
      "try to add unit 1657 from 40 th HD.  Usage up to 0.8082803229497758\n",
      "try to add unit 1657 from 60 th HD.  Usage up to 0.8356613709688882\n",
      "try to add unit 1657 from 80 th HD.  Usage up to 0.8543495276392941\n",
      "try to add unit 1657 from 100 th HD.  Usage up to 0.8590703083478004\n",
      "try to add unit 1657 from 120 th HD.  Usage up to 0.8590703083478004\n",
      "try to add unit 1657 from 140 th HD.  Usage up to 0.8676605175169966\n",
      "all done trying to reduce usage of unit 1657 final usage = 0.87849 209 sec elapsed 39 0 successful, failed patches, couldn't start= 120\n",
      "current avg and SD of usage are 0.99945 0.07582\n",
      "starting to increase usage for unit 1548 with usage 0.72968 . Total UU units tried = 3\n",
      "try to add unit 1548 from 0 th HD.  Usage up to 0.7296751648749782\n",
      "try to add unit 1548 from 20 th HD.  Usage up to 0.8329344442270329\n",
      "try to add unit 1548 from 40 th HD.  Usage up to 0.8962113625178517\n",
      "all done trying to reduce usage of unit 1548 final usage = 0.95126 300 sec elapsed 46 0 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99946 0.07506\n",
      "starting to increase usage for unit 1045 with usage 0.73354 . Total UU units tried = 4\n",
      "try to add unit 1045 from 0 th HD.  Usage up to 0.7335354949147261\n",
      "try to add unit 1045 from 20 th HD.  Usage up to 0.8204813054692919\n",
      "try to add unit 1045 from 40 th HD.  Usage up to 0.8835671569197611\n",
      "all done trying to reduce usage of unit 1045 final usage = 0.95161 440 sec elapsed 43 0 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 0.99949 0.07429\n",
      "starting to increase usage for unit 1650 with usage 0.73861 . Total UU units tried = 5\n",
      "try to add unit 1650 from 0 th HD.  Usage up to 0.7386124138154222\n",
      "try to add unit 1650 from 20 th HD.  Usage up to 0.8388770128942381\n",
      "all done trying to reduce usage of unit 1650 final usage = 0.9507 563 sec elapsed 40 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99949 0.07369\n",
      "starting to increase usage for unit 1737 with usage 0.74557 . Total UU units tried = 6\n",
      "try to add unit 1737 from 0 th HD.  Usage up to 0.7455675067590446\n",
      "try to add unit 1737 from 20 th HD.  Usage up to 0.8444686424669263\n",
      "all done trying to reduce usage of unit 1737 final usage = 0.95229 653 sec elapsed 37 2 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.9995 0.07303\n",
      "starting to increase usage for unit 1612 with usage 0.74757 . Total UU units tried = 7\n",
      "try to add unit 1612 from 0 th HD.  Usage up to 0.7475691593722302\n",
      "try to add unit 1612 from 20 th HD.  Usage up to 0.84573072343277\n",
      "try to add unit 1612 from 40 th HD.  Usage up to 0.9372855340953634\n",
      "all done trying to reduce usage of unit 1612 final usage = 0.95653 780 sec elapsed 38 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99949 0.0728\n",
      "starting to increase usage for unit 959 with usage 0.74837 . Total UU units tried = 8\n",
      "try to add unit 959 from 0 th HD.  Usage up to 0.7483685206431189\n",
      "try to add unit 959 from 20 th HD.  Usage up to 0.8115830497615767\n",
      "try to add unit 959 from 40 th HD.  Usage up to 0.9067317367069432\n",
      "all done trying to reduce usage of unit 959 final usage = 0.95048 922 sec elapsed 44 1 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99949 0.07191\n",
      "starting to increase usage for unit 1056 with usage 0.75581 . Total UU units tried = 9\n",
      "try to add unit 1056 from 0 th HD.  Usage up to 0.7558058298980926\n",
      "try to add unit 1056 from 20 th HD.  Usage up to 0.8049503008751483\n",
      "try to add unit 1056 from 40 th HD.  Usage up to 0.8601101278871612\n",
      "all done trying to reduce usage of unit 1056 final usage = 0.88589 1076 sec elapsed 27 1 successful, failed patches, couldn't start= 30\n",
      "current avg and SD of usage are 0.99949 0.07144\n",
      "starting to increase usage for unit 990 with usage 0.75616 . Total UU units tried = 10\n",
      "try to add unit 990 from 0 th HD.  Usage up to 0.7561606683158874\n",
      "try to add unit 990 from 20 th HD.  Usage up to 0.8012238476011085\n",
      "try to add unit 990 from 40 th HD.  Usage up to 0.9096146363796997\n",
      "all done trying to reduce usage of unit 990 final usage = 0.95416 1321 sec elapsed 43 5 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99949 0.0708\n",
      "starting to increase usage for unit 1538 with usage 0.75827 . Total UU units tried = 11\n",
      "try to add unit 1538 from 0 th HD.  Usage up to 0.7582728017550923\n",
      "try to add unit 1538 from 20 th HD.  Usage up to 0.8599749513470164\n",
      "all done trying to reduce usage of unit 1538 final usage = 0.95088 1455 sec elapsed 31 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99949 0.07049\n",
      "starting to increase usage for unit 1444 with usage 0.75745 . Total UU units tried = 12\n",
      "try to add unit 1444 from 0 th HD.  Usage up to 0.7574474449957648\n",
      "try to add unit 1444 from 20 th HD.  Usage up to 0.8439786275090211\n",
      "try to add unit 1444 from 40 th HD.  Usage up to 0.9470728355092005\n",
      "all done trying to reduce usage of unit 1444 final usage = 0.9549 1573 sec elapsed 37 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 0.9995 0.07024\n",
      "starting to increase usage for unit 3794 with usage 0.75935 . Total UU units tried = 13\n",
      "try to add unit 3794 from 0 th HD.  Usage up to 0.7593542140759888\n",
      "try to add unit 3794 from 20 th HD.  Usage up to 0.819954896827483\n",
      "try to add unit 3794 from 40 th HD.  Usage up to 0.8850774948005916\n",
      "all done trying to reduce usage of unit 3794 final usage = 0.95767 1622 sec elapsed 48 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 0.99951 0.06924\n",
      "starting to increase usage for unit 1049 with usage 0.76217 . Total UU units tried = 14\n",
      "try to add unit 1049 from 0 th HD.  Usage up to 0.7621682257043063\n",
      "try to add unit 1049 from 20 th HD.  Usage up to 0.8242389533295122\n",
      "try to add unit 1049 from 40 th HD.  Usage up to 0.9134294743145835\n",
      "all done trying to reduce usage of unit 1049 final usage = 0.95662 2010 sec elapsed 40 4 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99951 0.06863\n",
      "starting to increase usage for unit 1504 with usage 0.76524 . Total UU units tried = 15\n",
      "try to add unit 1504 from 0 th HD.  Usage up to 0.7652382928941078\n",
      "try to add unit 1504 from 20 th HD.  Usage up to 0.8620090983208094\n",
      "all done trying to reduce usage of unit 1504 final usage = 0.95023 2133 sec elapsed 32 1 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99951 0.06836\n",
      "starting to increase usage for unit 4325 with usage 0.7697 . Total UU units tried = 16\n",
      "try to add unit 4325 from 0 th HD.  Usage up to 0.7696991187177674\n",
      "try to add unit 4325 from 20 th HD.  Usage up to 0.8335323404621178\n",
      "try to add unit 4325 from 40 th HD.  Usage up to 0.8883399286051855\n",
      "try to add unit 4325 from 60 th HD.  Usage up to 0.922000186867357\n",
      "try to add unit 4325 from 80 th HD.  Usage up to 0.9453246389086991\n",
      "all done trying to reduce usage of unit 4325 final usage = 0.95153 2198 sec elapsed 58 0 successful, failed patches, couldn't start= 29\n",
      "current avg and SD of usage are 0.99952 0.06798\n",
      "starting to increase usage for unit 1052 with usage 0.77245 . Total UU units tried = 17\n",
      "try to add unit 1052 from 0 th HD.  Usage up to 0.7724520409481664\n",
      "try to add unit 1052 from 20 th HD.  Usage up to 0.8229742728148133\n",
      "try to add unit 1052 from 40 th HD.  Usage up to 0.9187715471978257\n",
      "all done trying to reduce usage of unit 1052 final usage = 0.95143 2427 sec elapsed 43 3 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 0.99952 0.06758\n",
      "starting to increase usage for unit 1008 with usage 0.76354 . Total UU units tried = 18\n",
      "try to add unit 1008 from 0 th HD.  Usage up to 0.7635407874962004\n",
      "try to add unit 1008 from 20 th HD.  Usage up to 0.84357959676084\n",
      "try to add unit 1008 from 40 th HD.  Usage up to 0.9433073890024821\n",
      "all done trying to reduce usage of unit 1008 final usage = 0.9559 2705 sec elapsed 32 0 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 0.99952 0.0671\n",
      "starting to increase usage for unit 986 with usage 0.77568 . Total UU units tried = 19\n",
      "try to add unit 986 from 0 th HD.  Usage up to 0.7756806806177458\n",
      "try to add unit 986 from 20 th HD.  Usage up to 0.844528432090466\n",
      "try to add unit 986 from 40 th HD.  Usage up to 0.9283066923733788\n",
      "all done trying to reduce usage of unit 986 final usage = 0.95136 3047 sec elapsed 37 0 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 0.99952 0.06678\n",
      "starting to increase usage for unit 1604 with usage 0.77782 . Total UU units tried = 20\n",
      "try to add unit 1604 from 0 th HD.  Usage up to 0.7778227088687103\n",
      "try to add unit 1604 from 20 th HD.  Usage up to 0.8735835895678492\n",
      "all done trying to reduce usage of unit 1604 final usage = 0.96487 3232 sec elapsed 36 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99952 0.06664\n"
     ]
    }
   ],
   "source": [
    "#FIRST PATCHING BLOCK - boosting underuse.  Works fine.  For some states, may want to do overpopped first; DANGER OF LONG STRINGS\n",
    "print(\"Here, we exchange in under-used, THEN shed over-used\")\n",
    "maxSD = 0.05  #0.07  #0.05   #adjust down if distro already tight\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "print(\"we will stop patching on individual underused units when their usage exceeds\",r5(stopMinUse))\n",
    "stopMinUse = float(input(\"enter updated stopMinUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "nSmallUsers = 5\n",
    "maxExchangePop = 0.05*aDP \n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "maxNtries = int(currSD * nUnits / nDistricts)   #try up to about 10% of the avg no of units per district\n",
    "attemptedSmallUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries: \n",
    "    #each round, find the (5) most underused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nSmallFound, smallUsers = 0,0,list()\n",
    "    while nSmallFound < nSmallUsers:\n",
    "        consideredSmallU = idx[idxNo]\n",
    "        if consideredSmallU not in attemptedSmallUs:\n",
    "            smallUsers.append(consideredSmallU)\n",
    "            nSmallFound +=1\n",
    "        idxNo +=1\n",
    "    UUUclusters = [ [b] + unitNbrs[b] for b in smallUsers ]\n",
    "    smallUnderUse = [np.sum([(unitUse[j] - 1.) for j in UUUclusters[i] ]) for i in range(nSmallUsers) ]\n",
    "    smallI = smallUnderUse.index(np.max(smallUnderUse))\n",
    "    UUU = smallUsers[ smallI ]  #pick the unit that centers cluster with least composite use\n",
    "    attemptedSmallUs.append(UUU)  #so we don't try this unit again in a future loop\n",
    "    UUUc = UUUclusters[smallI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    print(\"starting to increase usage for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Total UU units tried =\",len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUc) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    idx0 = np.argsort(uuuDists)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs): #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 20 == 0:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(set(UUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not (contig and complementContig): #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "            loopUUUc = [UUU]\n",
    "        if contig and complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in uuCandidates:\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if contig and cContig:\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            if abs(excess) <= 2.*maxGap:  #giving ourselves a bit more margin after exchanges\n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying to reduce usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "861f29d3-b727-49af-90a8-aa9a963e0741",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 0.99944 0.99952 and their SDs are 0.12336 0.06664\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 300 out of 7211\n",
      "working on HD 600 out of 7211\n",
      "working on HD 1200 out of 7211\n",
      "working on HD 1500 out of 7211\n",
      "working on HD 2100 out of 7211\n",
      "working on HD 2400 out of 7211\n",
      "working on HD 2700 out of 7211\n",
      "working on HD 3000 out of 7211\n",
      "working on HD 3300 out of 7211\n",
      "working on HD 3600 out of 7211\n",
      "working on HD 3900 out of 7211\n",
      "working on HD 4200 out of 7211\n",
      "working on HD 4500 out of 7211\n",
      "working on HD 4800 out of 7211\n",
      "working on HD 5100 out of 7211\n",
      "working on HD 5400 out of 7211\n",
      "working on HD 5700 out of 7211\n",
      "working on HD 6000 out of 7211\n",
      "working on HD 6300 out of 7211\n",
      "working on HD 6600 out of 7211\n",
      "working on HD 6900 out of 7211\n",
      "all done checking HD and complement contiguity for all 7211 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "77c07848-50c2-4fe9-8841-768b02bf4ac4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use threshold for intervention is 1.12\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated value 1.35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maxExchangePop is currently 0.02 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this block reduces overuse, with exchanges up to 15387\n",
      "current avg and SD of unit usage are 0.99944 0.0777 . Now trying to reduce up to 10 units' overusage\n",
      "starting to reduce usage for unit 6049 with usage 1.38283 . Total units tried = 1\n",
      "all done trying to reduce usage of unit 6049 final usage = 1.34917 3 sec elapsed 8 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99944 0.07757\n",
      "starting to reduce usage for unit 67 with usage 1.41963 . Total units tried = 2\n",
      "all done trying to reduce usage of unit 67 final usage = 1.34938 29 sec elapsed 11 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99943 0.07732\n",
      "starting to reduce usage for unit 6052 with usage 1.35754 . Total units tried = 3\n",
      "all done trying to reduce usage of unit 6052 final usage = 1.34744 40 sec elapsed 3 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 0.99943 0.07728\n",
      "starting to reduce usage for unit 920 with usage 1.43901 . Total units tried = 4\n",
      "try to drop unit 920 from 31 th HD.  usage down to 1.439006260463225\n",
      "try to drop unit 920 from 62 th HD.  usage down to 1.439006260463225\n",
      "try to drop unit 920 from 93 th HD.  usage down to 1.439006260463225\n",
      "try to drop unit 920 from 124 th HD.  usage down to 1.439006260463225\n",
      "try to drop unit 920 from 155 th HD.  usage down to 1.4230593280533474\n",
      "all done trying to reduce usage of unit 920 final usage = 1.42306 104 sec elapsed 4 0 successful, failed patches, couldn't start= 152\n",
      "current avg and SD of usage are 0.99943 0.0772\n",
      "starting to reduce usage for unit 6050 with usage 1.34322 . Total units tried = 5\n",
      "all done trying to reduce usage of unit 6050 final usage = 1.34322 109 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99943 0.0772\n",
      "starting to reduce usage for unit 1438 with usage 1.42325 . Total units tried = 6\n",
      "try to drop unit 1438 from 30 th HD.  usage down to 1.3871569589094985\n",
      "all done trying to reduce usage of unit 1438 final usage = 1.34787 172 sec elapsed 16 1 successful, failed patches, couldn't start= 38\n",
      "current avg and SD of usage are 0.99943 0.077\n",
      "starting to reduce usage for unit 6051 with usage 1.3287 . Total units tried = 7\n",
      "all done trying to reduce usage of unit 6051 final usage = 1.3287 177 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99943 0.077\n",
      "starting to reduce usage for unit 6275 with usage 1.3671 . Total units tried = 8\n",
      "all done trying to reduce usage of unit 6275 final usage = 1.34636 184 sec elapsed 4 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 0.99943 0.07697\n",
      "starting to reduce usage for unit 70 with usage 1.31349 . Total units tried = 9\n",
      "all done trying to reduce usage of unit 70 final usage = 1.31349 190 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99943 0.07697\n",
      "starting to reduce usage for unit 6021 with usage 1.31197 . Total units tried = 10\n",
      "all done trying to reduce usage of unit 6021 final usage = 1.31197 195 sec elapsed 0 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 0.99943 0.07697\n"
     ]
    }
   ],
   "source": [
    "#SECOND PATCHING BLOCK -- OVERUSERS.  WATCH FOR CREATING LONG STRINGS\n",
    "maxSD = 0.06 #0.06  #0.08\n",
    "stopMaxUse = 1.+  2.* maxSD  #0.5*maxSD  #don't be too aggressive; may create long chains\n",
    "print(\"default use threshold for intervention is\",r5(stopMaxUse) )\n",
    "stopMaxUse = float(input(\"enter updated stopMaxUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage above\",stopMaxUse)\n",
    "\n",
    "nBigUsers = 5\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP \n",
    "print(\"this block reduces overuse, with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        maxNtries +=1\n",
    "#maxNtries = int(currSD * nUnits / nDistricts)   #try up to about 10% of the districts a unit is drawn into\n",
    "maxNtries = 10 #overrirde\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "attemptedBigUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to reduce up to\",maxNtries,\"units' overusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedBigUs) < maxNtries: \n",
    "    #each round, find the (5) most overused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nBigFound, bigUsers = 0,0,list()\n",
    "    while nBigFound < nBigUsers:\n",
    "        consideredBigU = idx[-idxNo-1]\n",
    "        if consideredBigU not in attemptedBigUs:\n",
    "            bigUsers.append(consideredBigU)\n",
    "            nBigFound +=1\n",
    "        idxNo +=1\n",
    "    OUUclusters = [ [b] + unitNbrs[b] for b in bigUsers ]\n",
    "    bigOverUse = [np.sum([(unitUse[j] - 1.) for j in OUUclusters[i] ]) for i in range(nBigUsers) ]\n",
    "    bigI = bigOverUse.index(np.max(bigOverUse))\n",
    "    OUU = bigUsers[ bigI ]  #pick the unit that centers cluster with most overuse\n",
    "    attemptedBigUs.append(OUU)  #so we don't try this unit again in a future loop\n",
    "    OUUc = OUUclusters[bigI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    print(\"starting to reduce usage for unit\",OUU,\"with usage\",r5(unitUse[OUU]),\". Total units tried =\",len(attemptedBigUs) )\n",
    "    for u in OUUc.copy():\n",
    "        if unitUse[u] < 1.:\n",
    "            OUUc.remove(u)  #...drop any underused neighbors of the primary OUU from the target sheddable cluster\n",
    "    OUU2nbrSet = set( unitNbrs[OUU])\n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.union(set(unitNbrs[u])) \n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.difference({u})\n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if OUU in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(OUU2nbrSet) ) > 0: #the OUU or one of its overused 1-neighbors adjoins the complement\n",
    "                ouuHDs.append(t)  #so we can shed the OOUc to the complement\n",
    "                ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists)\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.5*len(ouuHDs): #arbitrarily only examine 50% of HDs, biasing farthest ones\n",
    "        idxNo -=1\n",
    "        if idxNo % int(0.1*len(ouuHDs)) == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD.  usage down to\",unitUse[OUU] )\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).difference(set(OUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        if not (contig and complementContig):  #dropping the cluster creates a problem (likely an HD discontig).  Try something simpler ...\n",
    "            if len(set(unitNbrs[OUU]).intersection(HDadjoinSet) )  > 0:  #Yay! The OUU itself on border.  Try dropping just it, not the full cluster\n",
    "                HDouuSet = {OUU}\n",
    "                trySet = set(HDunitList[t]).difference( {OUU} )\n",
    "                contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        else:\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(OUUc))\n",
    "        if contig and complementContig:  #we can at least drop the cluster, let's go for more\n",
    "            #HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD.  Defined above\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = False            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            while HDouuCpop < maxExchangePop and not giveUpOnShedding:\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDouuCpop += unitPop[shedU]\n",
    "                        #print(\"shedding unit\",shedU,\"from HD\",t,\"total shed Pop now\", HDouuCpop)\n",
    "                        HDouuSet.add(shedU)\n",
    "                        trySet.remove(shedU)\n",
    "                        del shedScores[shedCandidates.index(shedU)]\n",
    "                        del shedCandidates[shedCandidates.index(shedU)]\n",
    "                        newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                        for u in newSheddables:\n",
    "                            if HDouuCpop + unitPop[u] <= maxExchangePop and u not in shedCandidates:\n",
    "                                shedCandidates.append(u)\n",
    "                                shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True \n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...                            \n",
    "                    canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            if abs(currPop - aDP) <= 2.* maxGap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )\n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "cb60da52-ca13-45dd-8bf0-475213183a0d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overused units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"overused units\")\n",
    "maxPlot = 1.35\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > maxPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa912158-e8e9-46f3-8c3d-f6b17c26e62c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note - for FL, above is second run-through after passing first 10 thru without trying OUU alone-shed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0d4f1372-6044-4803-91d9-de4b95a40083",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 0.99944 0.99953 and their SDs are 0.12336 0.05906\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 300 out of 7211\n",
      "working on HD 600 out of 7211\n",
      "working on HD 1200 out of 7211\n",
      "working on HD 1500 out of 7211\n",
      "working on HD 2100 out of 7211\n",
      "working on HD 2400 out of 7211\n",
      "working on HD 2700 out of 7211\n",
      "working on HD 3000 out of 7211\n",
      "working on HD 3300 out of 7211\n",
      "working on HD 3600 out of 7211\n",
      "working on HD 3900 out of 7211\n",
      "working on HD 4200 out of 7211\n",
      "working on HD 4500 out of 7211\n",
      "working on HD 4800 out of 7211\n",
      "working on HD 5100 out of 7211\n",
      "working on HD 5400 out of 7211\n",
      "working on HD 5700 out of 7211\n",
      "working on HD 6000 out of 7211\n",
      "working on HD 6300 out of 7211\n",
      "working on HD 6600 out of 7211\n",
      "working on HD 6900 out of 7211\n",
      "all done checking HD and complement contiguity for all 7211 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ac9cd133-94f0-494b-ac74-0c584482d97e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"fullyPatched.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26b87c95-101f-4f8f-bb73-feb1827ac6b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))\n",
    "if noFrag == 1:\n",
    "    nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ec102553-e4f3-4010-86bc-725a01b95788",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter([v for v in range(len(parentVTDno))],parentVTDno)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "55d64c64-b473-49d0-b863-d4123cbb9b6c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for v in range(nVTDs):\n",
    "    if MAP.contains(vtdGeom[v].centroid) and v not in parentVTDno:\n",
    "        print(v,\"is in the map but not in the parent vtd list\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "ecd50c96-51ce-4d83-b0df-e8c645c35815",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after above patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on full or partial VTD master list for unit 0\n",
      "working on full or partial VTD master list for unit 2000\n",
      "working on full or partial VTD master list for unit 4000\n",
      "working on full or partial VTD master list for unit 6000\n",
      "Now converting unit lists to vtd lists for all HDs\n"
     ]
    }
   ],
   "source": [
    "#THIS WORKS FOR BOTH VTD- AND UNIT-BASED (FRAG VTD) -- **NEED TO ADD IN CUT DISTRICTS AND REBALANCE WEIGHTS\n",
    "print(\"after above patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))  #for simple states where units are at least whole vtd's\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist, unitFragVTDlist, unitVTDfrac = [list() for u in range(nUnits)], [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "unitFullVTDlist, unitPartialVTDlist =       [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "\n",
    "for u in range(nUnits):\n",
    "    if u %2000 == 0:\n",
    "        print(\"working on full or partial VTD master list for unit\",u)\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "        unitFullVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "            unitFullVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        if noFrag == 1:\n",
    "            unitVTDlist[u].append(allUnits[u] )\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitVTDlist[u].append(surroundedVTDs[i])\n",
    "        else:\n",
    "            unitFragVTDlist[u].append(allUnits[u])\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitFragVTDlist[u].append(surroundedVTDs[i])\n",
    "            vtdFrac = [0. for V in range(nVTDs)]\n",
    "            for vv in unitFragVTDlist[u]:\n",
    "                V = parentVTDno[vv]\n",
    "                if len(VTDchildren[V]) == 1:  #nonfragmented vtd\n",
    "                    vtdFrac[V] = 1.\n",
    "                else:\n",
    "                    if tractPop[V] > 0:\n",
    "                        vtdFrac[V] += fragVTDgeom[vv].area / vtdGeom[V].area #fragVTDpop[uu] / tractPop[v]  #we overwrote the frag pops earlier; can't use\n",
    "            for v in range(nVTDs):\n",
    "                if vtdFrac[v] > 0.0001:\n",
    "                    if vtdFrac[v] > 0.999:\n",
    "                        unitFullVTDlist[u].append(v)\n",
    "                    else:\n",
    "                        unitPartialVTDlist[u].append(v)\n",
    "                        unitVTDfrac[u].append(vtdFrac[v] )\n",
    "                                    \n",
    "HDpartialVTDlist, HDfullVTDlist, HDvtdFrac = [list() for t in range(nHDs)] ,[list() for t in range(nHDs)],[list() for t in range(nHDs)]               \n",
    "print(\"Now converting unit lists to vtd lists for all HDs\")\n",
    "if noFrag == 1:\n",
    "    for t in popHDlist:\n",
    "        for u in HDunitList[t]:\n",
    "            vtdList[t] += unitVTDlist[u]\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "else:\n",
    "    for t in popHDlist:\n",
    "        partialList, partialFrac = list(), list()  #for many HDs, we'll recover whole VTDs when aggregating units\n",
    "        for u in HDunitList[t]:\n",
    "            HDfullVTDlist[t] +=   unitFullVTDlist[u] \n",
    "            partialList +=         unitPartialVTDlist[u]\n",
    "            partialFrac +=          unitVTDfrac[u]\n",
    "        partialSet = set(partialList)  #non-duplicated set of partials across the HD, prepping to aggregate where possible\n",
    "        partialSetFrac, partialSetList = [0. for v in partialSet], list(partialSet)\n",
    "        for i,v in enumerate(partialList):\n",
    "            partialSetFrac[partialSetList.index(v)] += partialFrac[i]\n",
    "        for i,v in enumerate(partialSetList):\n",
    "            if partialSetFrac[i] > 0.999:  #the district has all the fragments of the original VTD contained in the HD's units\n",
    "                HDfullVTDlist[t].append(v)\n",
    "            else:\n",
    "                HDpartialVTDlist[t].append(v)\n",
    "                HDvtdFrac[t].append(      r5(partialSetFrac[i]) )  #save the aggregated fraction of this VTD across all units\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "5075814f-4a75-4807-ad8d-b3cc969cb470",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7211 7213 7211 7211 7211 7211\n"
     ]
    }
   ],
   "source": [
    "\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDvPop = HDvPop[0:nHDs]\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDfullVTDlist = HDfullVTDlist[0:nHDs]\n",
    "HDpartialVTDlist = HDpartialVTDlist[0:nHDs]\n",
    "HDvtdFrac = HDvtdFrac[0:nHDs]\n",
    "hdCPx, hdCPy = hdCPx[0:nHDs], hdCPy[0:nHDs]\n",
    "print(nHDs,len(tList), len(HDweight), len(HDvPop), len(HDvtdFrac), len(hdCPx) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "e385f357-5b7e-46f5-b054-c5c38d799395",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999998006 7211\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(np.sum(HDweight), nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "8d922813-2854-41d7-9405-22db5d5d5a59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now append the cut districts and adjust the HDweights\n",
      "26 2 are the number of HD-drawn and cut districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Now append the cut districts and adjust the HDweights\")\n",
    "print(nDistricts,nCutDistricts,\"are the number of HD-drawn and cut districts\")\n",
    "HDweight = [nDistricts/float(nCutDistricts + nDistricts) * HDweight[t] for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "if type(hdCPx) != type(list()):\n",
    "    hdCPx, hdCPy = hdCPx.to_list(), hdCPy.to_list()\n",
    "for L in allCutLists:\n",
    "    tList.append(len(tList))\n",
    "    HDweight.append(1./(nCutDistricts + nDistricts))\n",
    "    pop = np.sum([tractPop[v] for v in L])\n",
    "    HDvPop.append(pop )\n",
    "    HDfullVTDlist.append(L)\n",
    "    HDpartialVTDlist.append(list() )\n",
    "    HDvtdFrac.append(list() )\n",
    "    hdCPx.append(np.sum([tractPop[v]*tractCPx[v] for v in L]) / pop )\n",
    "    hdCPy.append(np.sum([tractPop[v]*tractCPy[v] for v in L]) / pop )\n",
    "    \n",
    "\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                        \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedWithCutsVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "b0b611f7-6c35-495d-b38c-c848317d63ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999998149\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "b9d808de-119f-4197-8e0c-8407c105d789",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Tpk1+jfv4448VFRWlyy+/vCeHBQAAYSjgMLJq1SrNnTtX8+fPV3l5uSZMmKD8/HxVV1d3O87j8WjatGm6/vrrezxZAAAQfhzGGBPIgHHjxmnMmDEqKSnxtmVkZGjy5MkqLi7uctydd96pCy64QJGRkXrnnXdUUVHh9zEbGxvlcrnk8XgUHx8fyHQBAIAl/n5+B3RmpKWlRWVlZcrLy/Npz8vL0+bNm7sc99prr+m///u/tWDBAr+O09zcrMbGRp8bAAAITwGFkYaGBrW2tioxMdGnPTExUXV1dZ2O+eKLL/TII49oxYoVioqK8us4xcXFcrlc3ltqamog0wQAACGkRz9gdTgcPv82xnRok6TW1lZ9//vf1+OPP64LL7zQ7/0XFRXJ4/F4bzU1NT2ZJgAACAH+nar4m+HDhysyMrLDWZD6+voOZ0skqampSVu3blV5eblmzZolSWpra5MxRlFRUVq/fr2uu+66DuOcTqecTmcgUwMAACEqoDMjMTExysrKktvt9ml3u93Kzc3t0D8+Pl7btm1TRUWF91ZQUKCLLrpIFRUVGjduXO9mDwAAQl5AZ0YkqbCwUFOnTlV2drZycnL0yiuvqLq6WgUFBZJOfMXy1Vdfafny5YqIiFBmZqbP+ISEBMXGxnZoBwAAA1PAYWTKlCnav3+/nnjiCdXW1iozM1Nr165VWlqaJKm2tvaUa44AAAC0C3idERtYZwQAgNDTJ+uMAAAABBthBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVPQojixcvVnp6umJjY5WVlaVNmzZ12Xf16tW68cYbdfbZZys+Pl45OTl6//33ezxhAAAQXgIOI6tWrdLcuXM1f/58lZeXa8KECcrPz1d1dXWn/Tdu3Kgbb7xRa9euVVlZma699lpNmjRJ5eXlvZ48AAAIfQ5jjAlkwLhx4zRmzBiVlJR42zIyMjR58mQVFxf7tY9LLrlEU6ZM0c9+9jO/+jc2Nsrlcsnj8Sg+Pj6Q6QIAAEv8/fwO6MxIS0uLysrKlJeX59Oel5enzZs3+7WPtrY2NTU1aejQoV32aW5uVmNjo88NAACEp4DCSENDg1pbW5WYmOjTnpiYqLq6Or/28W//9m86fPiw7rjjji77FBcXy+VyeW+pqamBTBMAAISQHv2A1eFw+PzbGNOhrTNvvvmmHnvsMa1atUoJCQld9isqKpLH4/HeampqejJNAAAQAqIC6Tx8+HBFRkZ2OAtSX1/f4WzJyVatWqUZM2boN7/5jW644YZu+zqdTjmdzkCmBgAAQlRAZ0ZiYmKUlZUlt9vt0+52u5Wbm9vluDfffFPTp0/Xr3/9a02cOLFnMwUAAGEpoDMjklRYWKipU6cqOztbOTk5euWVV1RdXa2CggJJJ75i+eqrr7R8+XJJJ4LItGnT9Pzzz+vKK6/0nlUZNGiQXC5XEEsBAAChKOAwMmXKFO3fv19PPPGEamtrlZmZqbVr1yotLU2SVFtb67PmyMsvv6zjx4/r/vvv1/333+9tv/vuu/X666/3vgIAABDSAl5nxAbWGQEAIPT0yTojAAAAwUYYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVUbYnYEvL8Ta98OGf9ML/req2X9rQwXr7vvEaekaMquoP6+bnS9XcahQTIf389tGKiHQoIS5WY9OHSpL+z5bdKnr3Tx32c1HCEM274SKt3PwnbdjzbUBzHZUcrzdmjNOur5tU33TUe7zICEeXY6rqD+vahRsCOk53nvofmZoybmS3x+yp1jbT5f0WKGeE9Mw/Xi5FyK/76VTz+rTqgN/3eVfj6zxHdOBwi4ae4VRSfM/m1Nu59FZXx++sXdIp52q7nmCwXcOho8c1b1W5qr85opFnDdIvplyhluNtuuU5t+qOnugTIek/Z03QqBHx/TavvhDM15J04v3/P7bs0d4D3yr1rEG6MCFOf9x7QJJDOecN05XfGebXfm0/B4KhuuFb3fx8qY4ca9Og6Aitm3ONRg4f3O/zcBhjTKCDFi9erGeffVa1tbW65JJLtGjRIk2YMKHL/qWlpSosLNSOHTuUkpKin/70pyooKPD7eI2NjXK5XPJ4PIqP7/2LqnhtpV7e2H0ICdSZg6N18NtjQd1nd5JdsVowaZRuzkzusO07Re+pLeBH1T8v/WBMp8fsqXXba1XwxmdB29/JurufurNue60ef7dStZ6jPdpXZ+N7OqfezqW3ujr+baOTtea/an3azxwcLUk+r4WT52q7nmCwXcNtL27S5182BjRmz9MT+2g2fSuYryXpxPv/q5uqun2PPHNwtJ7+3qXd7tf2cyAYzn/0PR1v69geFSH95angPF/8/fwOOIysWrVKU6dO1eLFizV+/Hi9/PLLWrJkiSorKzVy5MgO/auqqpSZmakf/ehHuvfee/Xxxx/rvvvu05tvvqnbb789qMX4oy+CiA3t2bvkpHDQl0GkXbACSV8HEanr+6k767bX6sdvfKaT70Z/99XV+JP35c+cejuX3vKnllP5+7lKslpPMNh+THoSRNqFWiAJ5mtJCvz9v6v3OtvPgWDoKoi0C1Yg8ffzO+DfjCxcuFAzZszQzJkzlZGRoUWLFik1NVUlJSWd9n/ppZc0cuRILVq0SBkZGZo5c6buuecePffcc4EeutdajreFRRCR5H0RPP5upVr/lj6q6g/3eRCRpH95Z7v3mD3V2mY0r4+DiNT5/dSd1jajx9+t7PTNz599dTf+ZKeaU2/n0luB1NKd9vGPrdmhx9bYqycYbD8mh44e73EQkaTKXoztb8F8LUkn3v9f3RTY+39n+7X9HAiG6oZvuw0iknS87US//hJQGGlpaVFZWZny8vJ82vPy8rR58+ZOx2zZsqVD/5tuuklbt27VsWOdf63R3NysxsZGn1sw/MeWPUHZz+nCSKr1HNWnVQckSTc/X9ovx2041OI9Zk99WnVAR4I0n1M5+X7qzqdVBzo9Hezvvk41PpA59XYuveVvLf4wkuoam1XXaK+eYLD9mMxbVd6r8be+uClIM+l7wXwtSSfe/wPNB53t1/ZzIBj8/azor88UKcAw0tDQoNbWViUmJvq0JyYmqq6urtMxdXV1nfY/fvy4GhoaOh1TXFwsl8vlvaWmpgYyzS7tPdB/Ka8/1TedeGE0t/ZfEm8/pq3xfXVMf+fVVb9A6+quf2/n0ls2HiObx/WH7cek+pveRfhT/M/waSWYryWp5+//J+/X9nMgGI4c8++Z4G+/YOjRpb0Oh++vhY0xHdpO1b+z9nZFRUXyeDzeW01NTU+m2UHa0P7/hXB/SIiLlSQ5I/vvV9ztx7Q1vq+O6e+8uuoXaF3d9e/tXHrLxmNk87j+sP2YjDxrUK/Gh9JaDsF8LUk9f/8/eb+2nwPBMCjav2eCv/2CIaAjDR8+XJGRkR3OgtTX13c4+9EuKSmp0/5RUVEaNmxYp2OcTqfi4+N9bsEwNefcoOzndOHQiV9vt19OuW7ONf1y3OFnxHiP2VNj04eqd2+r/jv5furO2PShSnbFqqtYd6p9tY8Pxpx6O5feOtXxA+GQlBR/4nJMW/UEg+3H5BdTrujV+P+c1fVVj6ebYL6WpBPv/4FeddvZfm0/B4LB38+K/vpMkQIMIzExMcrKypLb7fZpd7vdys3N7XRMTk5Oh/7r169Xdna2oqOjA5xu78RERejeq9P79Zh9pf2FsGDSKO917ekJQwJ+sfXEk5Mze30tfWSEQ7/429UVfamz+6k7kREOLZg0ymdsIPtqH+/PvXOqOfV2Lr3V3fED0T72sdsu0WO32asnGGw/JmfERumyXqwZEkrrjQTztSSdeP//0YTA3v8726/t50AwjBw+WFGn+PSPilC/rjcS8DmYwsJCLVmyRMuWLdPOnTs1b948VVdXe9cNKSoq0rRp07z9CwoKtHfvXhUWFmrnzp1atmyZli5dqgcffDB4VQSg6JZRfRJI2tdX6C9JrthOLx/bXTyxTwNJMNcZuTkzWS/1cSDp6n7qzs2ZySr5wRglnfR/Zf7uq318V/9XlxzAnHo7l97q6vjJrljde3V6hxrPHBzd4bXw93O1XU8w2K5hzawJPQokoXZZrxTc15L0/9//T/Ueedbg6G7f62w/B4LhL09N7DKQBHOdEX/1eNGzZ555RrW1tcrMzNQvfvELXX311ZKk6dOna8+ePdqwYYO3f2lpqebNm+dd9Ozhhx+2uuiZxAqsgWIFVlZgZQVWX7ZrYAVWVmANhr5egbXPFj2zoS/CCAAA6Ft9tugZAABAMBFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFZF2Z6AP9oXiW1sbLQ8EwAA4K/2z+1TLfYeEmGkqalJkpSammp5JgAAIFBNTU1yuVxdbg+Jv03T1tamffv2KS4uTg5H8P4IUWNjo1JTU1VTUzPg/ubNQK19oNYtUftArH2g1i0N3NpPt7qNMWpqalJKSooiIrr+ZUhInBmJiIjQiBEj+mz/8fHxp8WDZsNArX2g1i1R+0CsfaDWLQ3c2k+nurs7I9KOH7ACAACrCCMAAMCqAR1GnE6nFixYIKfTaXsq/W6g1j5Q65aofSDWPlDrlgZu7aFad0j8gBUAAISvAX1mBAAA2EcYAQAAVhFGAACAVYQRAABg1YAOI4sXL1Z6erpiY2OVlZWlTZs22Z5SQDZu3KhJkyYpJSVFDodD77zzjs92Y4wee+wxpaSkaNCgQfqHf/gH7dixw6dPc3OzHnjgAQ0fPlxDhgzRbbfdpi+//NKnzzfffKOpU6fK5XLJ5XJp6tSpOnjwYB9X17Xi4mJ997vfVVxcnBISEjR58mTt2rXLp0841l5SUqLLLrvMu5hRTk6Ofve733m3h2PNnSkuLpbD4dDcuXO9beFa+2OPPSaHw+FzS0pK8m4P17rbffXVV/rBD36gYcOGafDgwbr88stVVlbm3R6u9Z977rkdHneHw6H7779fUpjWbQaolStXmujoaPPqq6+ayspKM2fOHDNkyBCzd+9e21Pz29q1a838+fPNW2+9ZSSZt99+22f7008/beLi4sxbb71ltm3bZqZMmWKSk5NNY2Ojt09BQYE555xzjNvtNp999pm59tprzejRo83x48e9fW6++WaTmZlpNm/ebDZv3mwyMzPNrbfe2l9ldnDTTTeZ1157zWzfvt1UVFSYiRMnmpEjR5pDhw55+4Rj7WvWrDHvvfee2bVrl9m1a5d59NFHTXR0tNm+fbsxJjxrPtmnn35qzj33XHPZZZeZOXPmeNvDtfYFCxaYSy65xNTW1npv9fX13u3hWrcxxhw4cMCkpaWZ6dOnmz/84Q+mqqrKfPDBB+Yvf/mLt0+41l9fX+/zmLvdbiPJfPjhh8aY8Kx7wIaRsWPHmoKCAp+2iy++2DzyyCOWZtQ7J4eRtrY2k5SUZJ5++mlv29GjR43L5TIvvfSSMcaYgwcPmujoaLNy5Upvn6+++spERESYdevWGWOMqaysNJLMJ5984u2zZcsWI8n86U9/6uOq/FNfX28kmdLSUmPMwKr9rLPOMkuWLBkQNTc1NZkLLrjAuN1uc80113jDSDjXvmDBAjN69OhOt4Vz3cYY8/DDD5urrrqqy+3hXv/fmzNnjjnvvPNMW1tb2NY9IL+maWlpUVlZmfLy8nza8/LytHnzZkuzCq6qqirV1dX51Oh0OnXNNdd4aywrK9OxY8d8+qSkpCgzM9PbZ8uWLXK5XBo3bpy3z5VXXimXy3Xa3Fcej0eSNHToUEkDo/bW1latXLlShw8fVk5OzoCo+f7779fEiRN1ww03+LSHe+1ffPGFUlJSlJ6erjvvvFO7d++WFP51r1mzRtnZ2fqnf/onJSQk6IorrtCrr77q3R7u9bdraWnRG2+8oXvuuUcOhyNs6x6QYaShoUGtra1KTEz0aU9MTFRdXZ2lWQVXex3d1VhXV6eYmBidddZZ3fZJSEjosP+EhITT4r4yxqiwsFBXXXWVMjMzJYV37du2bdMZZ5whp9OpgoICvf322xo1alRY1yxJK1eu1Geffabi4uIO28K59nHjxmn58uV6//339eqrr6qurk65ubnav39/WNctSbt371ZJSYkuuOACvf/++yooKNDs2bO1fPlySeH9uP+9d955RwcPHtT06dMlhW/dIfFXe/uKw+Hw+bcxpkNbqOtJjSf36az/6XJfzZo1S59//rk++uijDtvCsfaLLrpIFRUVOnjwoN566y3dfffdKi0t9W4Px5pramo0Z84crV+/XrGxsV32C8fa8/Pzvf996aWXKicnR+edd55+9atf6corr5QUnnVLUltbm7Kzs/XUU09Jkq644grt2LFDJSUlmjZtmrdfuNbfbunSpcrPz1dKSopPe7jVPSDPjAwfPlyRkZEd0l99fX2HtBmq2n9x312NSUlJamlp0TfffNNtn6+//rrD/v/6179av68eeOABrVmzRh9++KFGjBjhbQ/n2mNiYnT++ecrOztbxcXFGj16tJ5//vmwrrmsrEz19fXKyspSVFSUoqKiVFpaqn//939XVFSUd17hWPvJhgwZoksvvVRffPFFWD/mkpScnKxRo0b5tGVkZKi6ulpSeL/O2+3du1cffPCBZs6c6W0L17oHZBiJiYlRVlaW3G63T7vb7VZubq6lWQVXenq6kpKSfGpsaWlRaWmpt8asrCxFR0f79KmtrdX27du9fXJycuTxePTpp596+/zhD3+Qx+Oxdl8ZYzRr1iytXr1av//975Wenu6zPZxrP5kxRs3NzWFd8/XXX69t27apoqLCe8vOztZdd92liooKfec73wnb2k/W3NysnTt3Kjk5Oawfc0kaP358h0v2//znPystLU3SwHidv/baa0pISNDEiRO9bWFbd7/9VPY0035p79KlS01lZaWZO3euGTJkiNmzZ4/tqfmtqanJlJeXm/LyciPJLFy40JSXl3svT3766aeNy+Uyq1evNtu2bTP//M//3OnlXyNGjDAffPCB+eyzz8x1113X6eVfl112mdmyZYvZsmWLufTSS61e9vbjH//YuFwus2HDBp/L37799ltvn3CsvaioyGzcuNFUVVWZzz//3Dz66KMmIiLCrF+/3hgTnjV35e+vpjEmfGv/yU9+YjZs2GB2795tPvnkE3PrrbeauLg47/tUuNZtzInLuKOiosy//uu/mi+++MKsWLHCDB482LzxxhvePuFcf2trqxk5cqR5+OGHO2wLx7oHbBgxxphf/vKXJi0tzcTExJgxY8Z4Lw0NFR9++KGR1OF29913G2NOXPq2YMECk5SUZJxOp7n66qvNtm3bfPZx5MgRM2vWLDN06FAzaNAgc+utt5rq6mqfPvv37zd33XWXiYuLM3Fxceauu+4y33zzTT9V2VFnNUsyr732mrdPONZ+zz33eJ+vZ599trn++uu9QcSY8Ky5KyeHkXCtvX39iOjoaJOSkmK+973vmR07dni3h2vd7d59912TmZlpnE6nufjii80rr7zisz2c63///feNJLNr164O28KxbocxxvT/+RgAAIATBuRvRgAAwOmDMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMCq/wdRWhvJGDwrRQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vtdUSE = [0. for v in range(nVTDs)]\n",
    "for u in range(nUnits):\n",
    "    for v in unitFullVTDlist[u]:\n",
    "        vtdUSE[v] += 1\n",
    "    for i,v in enumerate(unitPartialVTDlist[u]):\n",
    "        vtdUSE[v] += unitVTDfrac[u][i]\n",
    "\n",
    "plt.scatter([v for v in range(nVTDs)], vtdUSE)\n",
    "plt.show()\n",
    "unused = list()\n",
    "for v in range(nVTDs):\n",
    "    if vtdUSE[v] < 0.2 and MAP.contains(vtdGeom[v].centroid) and tractPop[v] > 0:\n",
    "        unused.append(v)\n",
    "        #print(v,\"in county\",countyNo[v],\"has pop\",tractPop[v],\"and map-total usage of\",vtdUSE[v],\"its surroundedness is\",v in surroundedVTDs)\n",
    "        plotPoly(vtdGeom[v].centroid.buffer(0.1))\n",
    "        plotCenter(v,vtdGeom[v],8)\n",
    "    #if vtdUSE[v] > 0.2 and vtdUSE[v] < 0.95:\n",
    "    #    plotCenter(r3(vtdUSE[v]),vtdGeom[v])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "404935e6-f0de-4b51-9db9-020aa208f852",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integrity check: vtd-based pops\n",
      "x = n surrounded, y= unit - vtd-based\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"integrity check: vtd-based pops\")\n",
    "HDvtdbasedPop = [0. for t in range(nHDs)]\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "nSurrs = [0. for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "        if u in surroundedVTDs:\n",
    "            nSurrs[t] += 1\n",
    "    for v in HDfullVTDlist[t]:\n",
    "        HDvtdbasedPop[t] += origVTDpop[v] #tractPop[v]\n",
    "    for i,v in enumerate(HDpartialVTDlist[t]):\n",
    "        HDvtdbasedPop[t] += HDvtdFrac[t][i] * origVTDpop[v] #tractPop[v] also works\n",
    "        \n",
    "plt.scatter([nSurrs[t] for t in popHDlist], [HDvPop[t] - HDvtdbasedPop[t] for t in popHDlist] )\n",
    "print(\"x = n surrounded, y= unit - vtd-based\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "64991323-d476-492e-8073-6bf4fb2dc945",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Additional forced patching with stubbornly overused units\n",
      "First, we ID these units and plot them.  Then, for each HD that uses them, we identify the district boundary units, and ...\n",
      "connect neighbor-by-neighbor from OUU until we hit the boundary.  We thus classify each HD by how many links it is from the boundary.\n",
      "We start with the group with fewest links to boundary.  Ranking these by OUU - hdCP distance, we grow from OUU and boundary toward each other\n",
      "Until they meet.  This defines a cluster to jettison, provided the remnant HD is contig, noEnclave\n",
      "unit and use 920 1.5239\n",
      "Here are all units with usage >  1.3\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"OPTIONAL additional forced patching with stubbornly overused units\")\n",
    "print(\"First, we ID these units and plot them.  Then, for each HD that uses them, we identify the district boundary units, and ...\")\n",
    "print(\"connect neighbor-by-neighbor from OUU until we hit the boundary.  We thus classify each HD by how many links it is from the boundary.\")\n",
    "print(\"We start with the group with fewest links to boundary.  Ranking these by OUU - hdCP distance, we grow from OUU and boundary toward each other\")\n",
    "print(\"Until they meet.  This defines a cluster to jettison, provided the remnant HD is contig, noEnclave\")\n",
    "maxUse = 1.3 #arbitrary usage for intervention\n",
    "OUUtargets = list()\n",
    "idx = np.argsort(unitUse)\n",
    "idxNo = 0\n",
    "while idxNo > - 0.5 * nUnits:\n",
    "    idxNo -=1\n",
    "    u = idx[idxNo]\n",
    "    if unitUse[u] > maxUse:\n",
    "        print(\"unit and use\",u, r5(unitUse[u]) )\n",
    "        OUUtargets.append(u)\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u],8)\n",
    "plotPoly(MAP,0.2)\n",
    "print(\"Here are all units with usage > \",maxUse)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ca62b4e2-a1b0-47d7-8c78-c49235abd6b5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This special block overcomes difficult-to-patch overused units\n",
      "working on reducing usage of unit 920 with pop 308 below 1.5205242130225334\n",
      "we found 338 HDs where the boundary was 0 units away from the offending OUU 920\n",
      "we found 0 HDs where the boundary was 1 units away from the offending OUU 920\n",
      "we found 13 HDs where the boundary was 2 units away from the offending OUU 920\n",
      "we found 0 HDs where the boundary was 3 units away from the offending OUU 920\n",
      "we found 0 HDs where the boundary was 4 units away from the offending OUU 920\n",
      "we found 0 HDs where the boundary was 5 units away from the offending OUU 920\n",
      "there were 0 HDs out of 351 with this OUU further from the boundary\n",
      "Now, lets reduce its usage some more\n",
      "try to drop unit 920 from 0 th HD.  usage down to 1.5205242130225334\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2743\n",
      "Hey! You said this was a boundary unit for HD, idxNo 1398\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5666\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5556\n",
      "Hey! You said this was a boundary unit for HD, idxNo 1380\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5757\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2323\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2341\n",
      "Hey! You said this was a boundary unit for HD, idxNo 3738\n",
      "Hey! You said this was a boundary unit for HD, idxNo 3735\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5252\n",
      "Hey! You said this was a boundary unit for HD, idxNo 1355\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2616\n",
      "Hey! You said this was a boundary unit for HD, idxNo 3728\n",
      "Hey! You said this was a boundary unit for HD, idxNo 3706\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2750\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2514\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2465\n",
      "Hey! You said this was a boundary unit for HD, idxNo 1365\n",
      "try to drop unit 920 from 20 th HD.  usage down to 1.5148584963076699\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5543\n",
      "Hey! You said this was a boundary unit for HD, idxNo 1404\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5548\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2379\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2374\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2607\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5641\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5690\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5699\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5727\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5541\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5331\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2440\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2269\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5279\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2539\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5334\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5258\n",
      "try to drop unit 920 from 40 th HD.  usage down to 1.4956673269351182\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2522\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2436\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5700\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2477\n",
      "Hey! You said this was a boundary unit for HD, idxNo 3732\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2518\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2821\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2468\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2381\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2498\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5681\n",
      "Hey! You said this was a boundary unit for HD, idxNo 3697\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2748\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2652\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2549\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2271\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2261\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2385\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5685\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5751\n",
      "try to drop unit 920 from 60 th HD.  usage down to 1.4956673269351182\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2670\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2319\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6984\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2317\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2523\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2830\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5730\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5692\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2601\n",
      "Hey! You said this was a boundary unit for HD, idxNo 7183\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2235\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2472\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2540\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2481\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2502\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2609\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2453\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2535\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5686\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6307\n",
      "try to drop unit 920 from 80 th HD.  usage down to 1.4956673269351182\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2551\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5753\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2332\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2373\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6595\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2452\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6322\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5301\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6284\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2511\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5721\n",
      "Hey! You said this was a boundary unit for HD, idxNo 7039\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5330\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6560\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6520\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5363\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6408\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6702\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6346\n",
      "Hey! You said this was a boundary unit for HD, idxNo 7010\n",
      "try to drop unit 920 from 100 th HD.  usage down to 1.4956673269351182\n",
      "Hey! You said this was a boundary unit for HD, idxNo 7012\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6951\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5436\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5271\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6960\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6631\n",
      "Hey! You said this was a boundary unit for HD, idxNo 7014\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5560\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6348\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5292\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6648\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2461\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6937\n",
      "Hey! You said this was a boundary unit for HD, idxNo 7023\n",
      "Hey! You said this was a boundary unit for HD, idxNo 3694\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6616\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6763\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5449\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2239\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5277\n",
      "try to drop unit 920 from 120 th HD.  usage down to 1.4956673269351182\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6637\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6529\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6638\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6788\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6365\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6609\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2610\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2538\n",
      "Hey! You said this was a boundary unit for HD, idxNo 6946\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5437\n",
      "Hey! You said this was a boundary unit for HD, idxNo 2469\n",
      "Hey! You said this was a boundary unit for HD, idxNo 5664\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[57], line 48\u001b[0m\n\u001b[0;32m     46\u001b[0m t \u001b[38;5;241m=\u001b[39m ouuHDs[idx0[idxNo]]\n\u001b[0;32m     47\u001b[0m trySet \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m(HDunitList[t])\u001b[38;5;241m.\u001b[39mdifference(\u001b[38;5;28mset\u001b[39m(OUUc))\n\u001b[1;32m---> 48\u001b[0m contig,complementContig, __, ___ \u001b[38;5;241m=\u001b[39m enclaveCheck(\u001b[38;5;28mlist\u001b[39m(trySet), unitNbrs)\n\u001b[0;32m     49\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (contig \u001b[38;5;129;01mand\u001b[39;00m complementContig):\n\u001b[0;32m     50\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHey! You said this was a boundary unit for HD, idxNo\u001b[39m\u001b[38;5;124m\"\u001b[39m,t)\n",
      "Cell \u001b[1;32mIn[2], line 714\u001b[0m, in \u001b[0;36menclaveCheck\u001b[1;34m(UNITLIST, UNITNBRS, maxLoops)\u001b[0m\n\u001b[0;32m    711\u001b[0m \u001b[38;5;66;03m#adjoinersOfAdjoiners = getAdjoiners(ADJLIST,  UNITNBRS)\u001b[39;00m\n\u001b[0;32m    712\u001b[0m \u001b[38;5;66;03m#BDRYLIST = list( set(UNITLIST).intersection(set(adjoinersOfAdjoiners)) )  #this might fail for queen-adjacent boundary units\u001b[39;00m\n\u001b[0;32m    713\u001b[0m BDRYLIST \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m (\u001b[38;5;28mset\u001b[39m(get2nbrs(ADJLIST,UNITNBRS))\u001b[38;5;241m.\u001b[39mintersection(UNITSET) )  \u001b[38;5;66;03m#in case inHD boundary is only queen-adjacent\u001b[39;00m\n\u001b[1;32m--> 714\u001b[0m noEnclave, enclaveList \u001b[38;5;241m=\u001b[39m   isContiguous(ADJLIST, UNITNBRS)  \n\u001b[0;32m    715\u001b[0m unbroken, smallPieceList \u001b[38;5;241m=\u001b[39m isContiguous(BDRYLIST, UNITNBRS)\n\u001b[0;32m    716\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m unbroken: \u001b[38;5;66;03m#could be that district's boundary intersects the state boundary or there is a nonHD enclave inside the HD\u001b[39;00m\n",
      "Cell \u001b[1;32mIn[2], line 687\u001b[0m, in \u001b[0;36misContiguous\u001b[1;34m(VLIST, NEIGHBORLIST)\u001b[0m\n\u001b[0;32m    685\u001b[0m newList \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m()\n\u001b[0;32m    686\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m V \u001b[38;5;129;01min\u001b[39;00m prevList:\n\u001b[1;32m--> 687\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m VV \u001b[38;5;129;01min\u001b[39;00m NEIGHBORLIST[V]:\n\u001b[0;32m    688\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m VV \u001b[38;5;129;01min\u001b[39;00m VLIST \u001b[38;5;129;01mand\u001b[39;00m VV \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m foundList:\n\u001b[0;32m    689\u001b[0m             newList\u001b[38;5;241m.\u001b[39mappend(VV)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "print(\"This developmental block overcomes difficult-to-patch overused units\")\n",
    "maxLevel = 6  #how many units away from the boundary is tolerable \n",
    "for OUU in OUUtargets:\n",
    "    print(\"working on reducing usage of unit\",OUU,\"with pop\",unitPop[OUU],\"below\",unitUse[OUU])\n",
    "    OUU_HDs = list()\n",
    "    for t in popHDlist:\n",
    "        if OUU in HDunitList[t]:\n",
    "            OUU_HDs.append(t)\n",
    "    HDsInLevel, tooFarFromBoundaryList = [list() for i in range(maxLevel)], list()\n",
    "    for t in OUU_HDs:\n",
    "        HDuSet = set(HDunitList[t])\n",
    "        boundaryFound, levelNo, nbrSet  = False, 0, {OUU}\n",
    "        if len(set(unitNbrs[OUU]).difference(HDuSet)) > 0:\n",
    "            HDsInLevel[0].append(t)\n",
    "            boundaryFound = True\n",
    "        while not boundaryFound and levelNo < maxLevel:\n",
    "            levelNo +=1\n",
    "            newNbrSet = nbrSet\n",
    "            for u in nbrSet:\n",
    "                newNbrSet = newNbrSet.union(unitNbrs[u])\n",
    "                if len(newNbrSet.difference(HDuSet) ) > 0:\n",
    "                    if boundaryFound == False:\n",
    "                        HDsInLevel[levelNo].append(t)\n",
    "                    boundaryFound = True\n",
    "                    break\n",
    "            nbrSet = newNbrSet\n",
    "        if not boundaryFound:\n",
    "            tooFarFromBoundaryList.append(t)\n",
    "    for levelNo in range(maxLevel):\n",
    "        print(\"we found\",len(HDsInLevel[levelNo]),\"HDs where the boundary was\",levelNo,\"units away from the offending OUU\",OUU)\n",
    "    print(\"there were\",len(tooFarFromBoundaryList),\"HDs out of\",len(OUU_HDs),\"with this OUU further from the boundary\")\n",
    "    print(\"Now, lets reduce its usage some more\")\n",
    "    \n",
    "    stopMaxUse = 1.1    \n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in HDsInLevel[0]:  #lazy here; plenty of HDs with the OUU on their boundaries \n",
    "        ouuHDs.append(t)  #so we can shed the OOUc to the complement\n",
    "        ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists)\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.8*len(ouuHDs): #arbitrarily only examine 80% of HDs, biasing farthest ones\n",
    "        OUUc = [OUU]  #again, being lazy in only interrogating the HDs with the OUU on thier boundaries\n",
    "        maxExchangePop = unitPop[OUU] + 0.02 * aDP  #allow less exchanging here than normal, since we focus on single OUU's\n",
    "        if idxNo % 20 == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD.  usage down to\",unitUse[OUU] )\n",
    "        idxNo -=1\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).difference(set(OUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        if not (contig and complementContig):\n",
    "            print(\"Hey! You said this was a boundary unit for HD, idxNo\",t,idxNo)\n",
    "        if contig and complementContig:  #we can at least drop the cluster, let's go for more\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = True            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True  #different than usual -- only shed this problematic OUU\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True\n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "                    canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            if abs(currPop - aDP) <= 2.* maxGap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4751e64b-c436-4c7b-89fd-ef839532babf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "338\n",
      "338\n",
      "[ 15  14  16   4  20  57  17  18 106  86]\n",
      "[183, 185, 214, 215, 216, 223, 1355, 1361, 1365]\n"
     ]
    }
   ],
   "source": [
    "print(len(HDsInLevel[0]))\n",
    "print(len(idx0))\n",
    "print(idx0[0:10])\n",
    "print(ouuHDs[0:9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a4d9f6e6-3d66-44ee-8b3b-46fb5a95448e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49 289 can, cantShed\n"
     ]
    }
   ],
   "source": [
    "canShed, cantShed = 0, 0\n",
    "for t in ouuHDs:\n",
    "    contig,complementContig, __, ___ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    trySet = set(HDunitList[t]).difference(set(OUUc))\n",
    "    contig2,complementContig2, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "    if contig2 and complementContig2:\n",
    "        canShed +=1\n",
    "    else:\n",
    "        cantShed +=1\n",
    "print(canShed, cantShed,\"can, cantShed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b22bad3e-039c-469f-a6ef-e16b784d363e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[183, 185, 214, 215, 216, 223, 1355, 1361, 1365, 1369] 118638\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(ouuHDs[0:10], len(ouuDists) )\n",
    "plt.hist(ouuDists)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef27af54-daf5-4e8f-9230-bce5b0b1e705",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "3ad3342b-b8e3-46f5-a3e5-f49df386b88f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\n",
      "Let's read in the 2020 and 2016 voting data for FL\n",
      "In year/election 2020 Dem and GOP total votes were 5296164 5667685 for a stateGOP of 0.51694\n",
      "There are a total of 7211  vote-mapped voting districts from election(s) in year  2020\n",
      "In year/election 2016 Dem and GOP total votes were 4500465 4615841 for a stateGOP of 0.50633\n",
      "There are a total of 7211  vote-mapped voting districts from election(s) in year  2016\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\")\n",
    "#note: we started w possibility of separate election --> 2020 vtd files by year.  Now we use ALARM combined 2016+2020 --> 2020 csv's for all exc CA\n",
    "# copied from \"HDpartisanAnalysis\"\n",
    "print(\"Let's read in the 2020 and 2016 voting data for\",STATE)\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G16PREDCLI\"], [\"G16PRERTRU\" ], list()\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G20PREDBID\", \"G16PREDCli\"], [\"G20PRERTRU\" ,\"G16PRERTru\" ], list()\n",
    "DemCandidates, RepCandidates, vestDFs = [\"pre_20_dem_bid\", \"pre_16_dem_cli\"], [\"pre_20_rep_tru\" ,\"pre_16_rep_tru\" ], list()\n",
    "nYears = 2\n",
    "unitIDs, vestReps,vestDems,yearLean = [list()]*nYears, [0]*nYears, [0]*nYears, [0.5]*nYears\n",
    "years = [str(2020),str(2016)]\n",
    "DemVotes, RepVotes = [list() for y in years], [list() for y in years]\n",
    "vestDir = \"./state_map_files/\" + STATE.lower()\n",
    "vestDF = pd.read_csv(vestDir + \"_2020_vtd.csv\")\n",
    "mergedDF = vestDF.copy() #pd.merge(mergedDF,vestDF, on = \"GEOID20\")\n",
    "for y,year in enumerate(years):\n",
    "    DemVotes[y], RepVotes[y], unitIDs[y] = mergedDF[DemCandidates[y]], mergedDF[RepCandidates[y]], vestDF[(\"GEOID20\")]\n",
    "    vestDems[y], vestReps[y] = np.sum(DemVotes[y]), np.sum(RepVotes[y])\n",
    "    yearLean[y] = vestReps[y]/float(vestDems[y]+vestReps[y])\n",
    "    print(\"In year/election\",year,\"Dem and GOP total votes were\",int(vestDems[y]),int(vestReps[y]),\"for a stateGOP of\",r5(yearLean[y]) )\n",
    "    nVTDs = len(DemVotes[y])\n",
    "    print(\"There are a total of\",nVTDs,\" vote-mapped voting districts from election(s) in year \",years[y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f628069d-4177-401e-bf49-b7f194d0ef8b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "5cf73783-4a48-4a3f-a2e3-c1b9ff32c859",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let's read in our ensemble of HD districts for FL\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the filename with the vtd assignments to districts; e.g. MO1654contigPatchBoth.csv FL7211patchedWithCutsVTDs.csv\n",
      "enter the number of HD-drawn districts in the state; e.g. 8 28\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This ensemble or enacted map describes 7213 drawn districts.  This state has 28 districts per map\n",
      "converting vtd list strings to vtd lists by drawn district ...\n",
      "the total weight across all rows should be 1.000, actually is 1.0\n",
      "I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\n",
      "translating from HD order of precinct rows to VEST order\n"
     ]
    }
   ],
   "source": [
    "print(\"Now let's read in our ensemble of HD districts for\",STATE)\n",
    "infilename = input(\"enter the filename with the vtd assignments to districts; e.g. MO1654contigPatchBoth.csv\")\n",
    "#infilename = \"GA_nD14tol0.01nW4.csv\"\n",
    "HDdf = pd.read_csv(\"2024state_HD_output/\"+infilename)\n",
    "nRows = len(HDdf)\n",
    "nStateDistricts = int(input(\"enter the number of HD-drawn districts in the state; e.g. 8\"))\n",
    "print(\"This ensemble or enacted map describes\",nRows,\"drawn districts.  This state has\",nStateDistricts,\"districts per map\")\n",
    "\n",
    "HDvPop = HDdf[\"HDvPop\"].to_list()\n",
    "HDweight = HDdf['HDweight'].to_list()\n",
    "#tractPop = HDdf['tractPop'].to_list()\n",
    "#statePop = np.sum(tractPop)\n",
    "tractCPx = HDdf['centroid x'].to_list()\n",
    "tractCPy = HDdf['centroid y'].to_list()\n",
    "HDvtdListString = HDdf[\"HDvtdList\"]\n",
    "print(\"converting vtd list strings to vtd lists by drawn district ...\")\n",
    "HDvtdList = [list() for t in range(nRows) ]\n",
    "for t in range(nRows):\n",
    "    if HDvtdListString[t] != \"[]\":\n",
    "        HDvtdList[t] = ast.literal_eval(HDvtdListString[t])\n",
    "\n",
    "if \"partials\" in HDdf.columns.values: #\"splitTractNo\" #.to_list():\n",
    "    splitTractList, splitTractUseList = HDdf['partials'], HDdf['HDvtdFrac']\n",
    "    splitTractNo = [ast.literal_eval(splitTractList[t])    for t in range(nRows)]\n",
    "    splitTractUse = [ast.literal_eval(splitTractUseList[t]) for t in range(nRows)]\n",
    "else :  #incoming file didn't use any partial units, only whole ones\n",
    "    splitTractNo, splitTractUse = [[] for t in range(nRows)] , [ [] for t in range(nRows)]\n",
    "\n",
    "print(\"the total weight across all rows should be 1.000, actually is\",r5(np.sum(HDweight)) )\n",
    "print(\"I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\")\n",
    "censusGEOID20 = tractPopFile['GEOID20']\n",
    "miniHDdf = pd.DataFrame( {\"GEOID20\":censusGEOID20} )\n",
    "vestNo = [v for v in range(nVTDs)]\n",
    "print(\"translating from HD order of precinct rows to VEST order\")\n",
    "miniVestDF = pd.DataFrame( {\"GEOID20\":mergedDF['GEOID20'], \"vestNo\":vestNo} )\n",
    "mappingDF = pd.merge(miniHDdf,miniVestDF,on=\"GEOID20\")\n",
    "vestID = mappingDF['vestNo']  #this maps each named unit in a HDVL to the correct vestNo row with voting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9c38b293-7a46-4106-a972-b4868206bc2d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "540c6d1d-7728-48bf-90f0-f124b25ac7c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sanity check - Dem-leaning vtds for year 2020\n"
     ]
    },
    {
     "data": {
      "image/png": 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u6KN/QCIzxYpKnQjy+MgaXUwC4QtWXdN+RFFExjvhOq99EIX28vkujMFmFGFmDr70c4JTMslfcMvIuvW7djL3putIiowfs43T50I5nGMkzpxI3AE9HschOtmPjI+mhjqMM2ag6jPS+9ph8u6ZN7Jt3OJ4ml5tob2qmZisBFbeuZzje3bTa20h1hiDxd2PbPByzqIg1VxH+V4vZkFLd6uBmpJjOPrn0K85TigLr+r8OMrOEbxmzVW1DRDgw0SDpYHK/koaLY3khudS2VfJwriFZIVN7LQfIMDV8F8nPgDi4uJwuVxUVlYSHx+PyTRxXo0eYx/Bqnj6T+xAdrvQpWYTnDkfUfSftkTJi7u3F/1lwoM9DheOAR2CrEZUjnVyldxeMvBw/X03Yne4CDEZ+dmLW8iI6sQjQVVLD9/71M2T9Hx52nu7ONlYy/ahPO7RJxFtcvHQjw4TER3J756WefdtM0f2t+Jt6IZLsuO7Btvo7O/EJ/uQvG6EwTZ8gogPAY9PZrC6gcGwc4iiiKhQICoEvN4hLD119HXVodEFY7ho6sPnclBz9Agtx9uImj4DhVJFZNGscU6/82/wR7Cc3foKh7f8lVmrH8Rmd9Bc08P1S6LGtJUkGcUlkeLqRi3B8XNQmILQpMfT3f02eXl3AFC399y4czT3vjk0l9Zx9N19zLl1MbOWLsPr9XL2+FEaGhu575FP0D4Ep09D5gJ48KvQ2Ggj5fDt6EIFRI0Cl9VGbWkNA/WNIFwYj99aJAiiP6JIklF43MQP7CL+hqsTINdSWyZQhybAPxtZlpFkCYWooLKvkqWJS9Ek+1+cFsQtYFvDNryylyh9FGateUyyxAABrob/SvEB/vwiubm51NbW4nK5iIyMRJZlBgZa0etDUal0aLVqkor8FgBndwPO9gZa3/ol2sQ0VLEZ9L/+BtkPPjjpPip2nsIreZl/9wrECdJzO/uGUBt0qNUq1Gq/leMbD60dWf/jTQd48kgtn5yWgFk3GlorT2CtuJi2ni7eenUvISaBn9y4khtPVFCcNoPO3irumG/EpezHF3KEHncuvRGwr76cxSmjIcFnNYlk9NQOiwsVgikeUVSgRkblkyjOuY/lEWZkyYfP40PySSgxkVn4IPahIarPbSI37076zr6GqFBhG+ogZsEyEvNW0bx9G6qoaM78+Y9M/8znAKh9921cQ4PYentQajRog0PYXmfjqHofwoCS2JSZ/OSFs6wtimNOgd/S1NHdTXjoaPbYhvpqDAsWIxqD8Xb1YztxnqaefmzbtiADzjY7NRXniI2MRIuC9Nv9FouEvFQMYcGU7SnxSxlBQIsRS0giL5dW4EyR2fdsIgUFOmCIpUt9FBQYOHQIdne76fz924imIHRFMwjXqbgvd6wPyOtnD9DrsCBLGjYNDPA9yYNK/OAWrSsJjcpKePxxMJlG2wTESYBr5eWKl4nUR6IUlKgVajSKsRbb1cmrabO20TLUwsHWg4RqQmkeamZh/EJsHhs55g9ecmMyAtfxfxb/teLjAmlpaRw79keaW6JAFjAGhdHaUkV4eBLJyTNG2mkjktFGJBOSvwR7WxW2yhLsnzRetm99eBCW7v4JhQeAPjoUo9lM+Ts7yLltfCrxaLOWB2cm84eTTXgBnVJEKQqUNjdgzRukqu40dtsgCAKiQiQ1MZfOASutLd2EJegxOXWkhoVwj7WTZ5/9LLMjVtNQl01mQjQhwhQSouGxFcn88djGMeJDE55GasbEYaEOtxdduJKo5Ml9ZoLNwbRUHSDSnEtYwUr6K8pxdXehUKlJvt7v8+CyDlH+8gsggzE2jrRbbx/ZXpZlQl/ZxheXj/qpuN1ent9YOSI+jtee4vpZy0bWN59tI2nmNFySQL/FgxgRTLJPSf7qxePG17jl+MhnyeVB73SRJAhINieSxweSjFkXiUc00e91cvcTHbz5RCe3L6rlC48/yJkzft+P5Ll5fPy+UbH42+LxNV76nEN8dvYNALQMtdBoaSQ9dOL6PReyp7a2+jOozh7ObzbRTffSTKsWC6xbB9/85oRdj3A14uVKN/jAQ+A/G4fXgcvnYmHcQnRK3aRWjVhjLLHGWKZFThtZ9lrla8QZ49hav5Uccw6JwddWAmIyLhbVAf4z+K8XHwD5+TditVZgNi9GFDVkpC+gvHwrMTETq3d9bBYunZHg2jZkWZ70x5k0LZ3SncVjlrncXtQqBYIg0FNZjzY8BEXXBKGtgMc6hFqp4Euz/QnPfJKE0yehrjdz+MQWkpKySYzPQJZkWmrreW/jBmYWLeb6pYuxDtrZ8s4eANaYdCjEBZgLg3j48WZmFArMnZN60Z6u3mSqEgVae4fYv+FtknWmkXou6pAwwvNyUOl1qK0CluPHSb3/q/7eRRGksYnLEpYsm6h7AGRJ4mBND18c/vuZHe+gUakwhw5RUdLgPxeOco7U1Y1sczheTwgFfL12F18LGiIoOAq74wwwXnz42prof3vYOVYhoIoKRZMRj2gyIqqUoFJw4A9b+OzsdO43+y0Z5zLaaGhdQs+ZX7Hn3Txc/XEw2HDV5w0gPiieo+1Hqa6vZk3ymnHXzYXsqT/+MXz846PLvV7Yuxfmzh0VJRfaPvggPPwwNDZCXx9s2wZ2O9x8M9xxx8Rp4KuqIDgYjh//14mIgGD56LC1fitL4pegV11bnhyAe7LvGfl8sPkAZ/YfZkXKSmyv/A2F0EPXYx9DoxNJNGVcVZbpyQR5gI8+AfEB6HQJqNXhdPfsRiFqCQ2dw9SpN1x2G4MhlKGhQbZsXU5U5EpUqmQuzPX7JA+ZGTdjsdTR3rYR9SYb4elJuMLCeb28nViNisIYI+UvvMS0m27yZwmdAKfaH5Z5QeD0u4fY2FpFs2Dj44s/MaZt3b4KHl33GIIgYBkY5I2/bOa625Zxcn8FdkcoHUf2kz59kA2fquFQUzGrv/biyLY+aayjqHyZ6i5KpYK7C4LosKSTmDKcL0WWcXW10nnsKB67g9OyFa8jn7jeNuLDokAhTujYOhnv7D+DNiKMTXXd7KluRWex8qO7xk5vTWHs99Neu5+8uBDu8USh7pIRFRoydD20b/g/fDKotAa//wUyakstoY88ddkx3BCnZ4p51LL1xUfN3POQjzMVXyY7GzShIIjdVzyWS6fI5sTMweq2srl+M6uTV6Mc9h/yeuGJJ/yBTENDEBbmFxwrVoBK5RcLzz8/KkoOH4aTJ/3/zp0Dnw80GoiO9t+om5ogKwuMRsjN9T/4V6zwCxGHw//vAldTr+ZKbQLi4j8Dl8+FR/JMap27WmzWIYRyO8uW3YBWrULMd9JkaeCtkruwy0pEdTyiqCRIIXF9/o8x66M42XmSIHUQAgIh2hCyTLksWACdnXDDDf5rPcB/DgHxMYxCoSMqci2S5KKp+QWMhkxCQmahVE4cpaFW61my+Pv4fA7Oln6S/PzRrKWS5KW09FWcrn6mzF2ELHuxNHZw/NhJvvTg7ciyzJ7dx+kpyCRx3rQJ+99SeYY9Ljveiv14ZCU6UcKo0rEmJoOzDVXj2lv7bHTWdxCdGoOtx0dm4lQs/UNkTUvCGKzncFs7r+m6wdZPWNJ0TvV38WrjWZL0Rpox8ZuzzaN9aS7/RuL2ulErLvJbEAQ0UfGEm8PB0kJP1xky567g2Cu/Qq3VE2QKo+O997A2NoIgIMgy8uAgyCCEjLWj9re0U6028di996JTKclMaGf1cH6SySjuqiRaG8Q7VR0cbnGBTs/iocMIRiMxK786/lxZfjtpX+3VZ7D1dtDXaxmzPC4uiq99fSf33zWadv/Vk17qeztQCaAGfLZe+vuMiAoFCqUShaicsE6PUW1kafxS9mzcwMJs/7G9s1VFarSaKek+EqMkNq9XotZI/O53LSxZkozDAU98qwO9yshgj49jRzTUN+pJTnbh82lwuwUcDoHycnC7/QImN1egpASsVjAY/FNFu3f7xyDLcOutY60oF1fzvVRMTNTmSlxtEb4AHx7UohqvNHHUWt/f/oY6JRVFkBHdtGlj1kleidryc3S1taE16vF6PRQtW4p6ODngjtyHOFe5mW/f4M/30zlUT4guirePfxqNUkfzUDNF0UWE68LZdmADHcpmXnqlEbW8kp9+2UVfj0y9VoPPIuOotCFqFGhSAnMwH2UC4uMSRFFDctKj+HwuBgaOIkluTKZC1Oowf6n3xj+NeZsVRNW4qmmiqKSgYPRN/fiuJ0jPv5uo5HD++Nrfuef6G1i2fDZv7raN2a6/p5sXjz1DjCESnTGWn88qItsUOW6MZy/6LEsSCAIzbsinvaqO8jM/wxgyhZikm8icETPSbndONouNRmwh+XhUBnocXXwlcwbR+rBx/f+ktu2y50ivUFDaWU9W4ui01PHTzyN5bJxs2sfyaY/QQh/78kR6Dr3M3MKbyf7K18b0YTt8BEQBw5zR/CGlh49hUQfx+L13jCwL0U3lQGcNbq+LjLDxb2OdQ318fWc7N2em0NrczS9uWcqpna8RogBhyXcuexwTUVO8k+SFawnVBfG7g7upay+hQBXJzORs+uxjb8pLEtJwD3Vgl0Eue53VCavoqTuFJPnA50PyeaBvP3DTuP00N9cRk52CNtOf/bbpHbj/k/DGG7D7JPQPechLVXLuXAZTp0Jj3SApqQ6270tAkiA+dgC1ykttrRav119PRhDA45EBgcoKia9+FZYsbKWjI5TWVh2SBMnJbpqatMgydHd7aCg/zLuDkVScj+QTd3mJD9PR3qzjC49BcJDMqSN9DJlV7DluIC1dpqZLJj1bycF9ajwugXvvFyYVFx9EsAT491LWUzaps6g6JRXjwgVYDxycYK1MU1UN+bPnE5E4/p4VpVVB4kz+uHkzn16zhqggv4jPjLuB1u5dLMwYLU/wdN0zfDbu0zTWqikokDjXaeTOO+HVnRBuAkFhRxV17VNCAT5cBMTHJCgUGszmxciyREfnBoKC8mhrfYXY2PswGv0PQVmW8PlcnGm203ZiJwgiPknCK0lcP30xOo3fQ9zoSGGoZJBUIGHKdF7d8B7Xr1w9kh68p72N0yeOI7ndZGXMQK1SszxnfFKwS9n1/J/RGYw0VOwgZ3khCrUBjSESvTGcjJljHUIfnpLAyU4TN6VcObma9gphc6HmBITG8jHLbM5+ls75MnlT7kSrDeVEVwk3Zt1KdLyKI6c2UFtX4p/OcbmZ2iwgt3cScttouvX+/gFqn3+Rpb/82Zh+k4ITaDn+EoquAwxqjcgCyJKHQZuFY/GrUQgiN+SkEO1zUyeWcqBehaJlE86YBHSTFMirGGhEXv9nv7/KJYeqCwolIT6HhHhYAxw8fo4Fsx6krLyc9p6+MW1jK970fxAAczxMG2+h6TvePG7Zvop9dJ1vIdeUS3nbaQA0LhM7N2j5zieb2LAzmrcG9SxYEE5NDWRkQFWljpdei8ft9WIOd9I/aMAnicOWFQUgIMsCFw5ozlwF+/f7KC1PICVjkCGrQGRsP253OIIAkgRarQqFQsusxbkcPdZL9dF67v1aIQqFQHg4zJ6norpziDvnZNIyQ+JkiYxG6ealv/ciuSJ48xk3qeEKfvDATl4pTeNQawhUj14XNUeTKIh3cGajTHxYEtYBLU3nbFRV+fjjc/CrnzShUKlQabWotTpUGi0qrRaVVodCqQyEb/4bKO0p5Zb0WyZc52lrw3rgAJb3NmBYMH/M9yMqFRRdt5ITz54iLWUIj8uHZPOQ9XAuADNSw/jSzj1YI/T88o2/8sVb70et0RFHJpX9O3nzyJdRdE0nJDqCz6Z8HPOhZgpnrKbTIVJZ6begyTKkRrtRBKsR9f94DqQA/14C4uMKCIJIcFAB1fVPk5b+TYzasDHrlEodibFLCTFoSYnKorISvvENL9/p7sRoUBNsklj6aRcPp0cQn5zC+Vf2sKA/im1v7MMQ7uHYrp309fWy6tbbUQwnzHrj+BuXHVPX2Up2N/2JjFnzSMzNJ+ZcFH3t1eQuuheFQkNFySaqS3eiM4TifyMWERBpaXNgTTNh1E5eku4PJxsZsLogNWbSNgDCJaF3F3wXDEZ/Pg635MagMpCcUUhyRuFIu8oT2xAKEolJGn1VPrpzD5a6elY+9QsMxrHTXId3/IbEKbeQED/6NtZj6WbvuYPclzwPpULFb8420+Os4idrbuN7B3/Ct+/4JXuP/ZI1LjuecweQ7C468eAWRGSfxFBWGMuWTF4I7mIu+L9MzcnhRxnp0HISBltBpQPXIKz60VX1c4HyulLKqk/x4JJ1NLcG8/Ofi7zwgj+PyD33wP/8QEdyRD0xoa2UlgZTX69GrZIpyOjB5hTp7NUzYFdi6ZPxer3I8sXTXxIMC5CWWgsmo0hcRg8rP7OdU8Ufp7MlDFH0Cw+/5UPAaZtKzLYyPGRwx9fm4bBLKEQvokKB0+nD4wvl4YcFnntOwTtvwR1rfaSkhOLyqvnlC2q+9S0Qg/XMujGHQzVAxmiocfocf46UOTk7KO4swBgCiblaqmua8DhVpEyfic/rwe104nE68Dhd2C0DuJ1OfF6P//tv2I82PY6o4SKDAgKWw+fIySi8qHYwYz5LshJdcBI6oxqtQYVar0QQ+P8uZv4T/F7WpKxhS9279PafIj44BZPGxJzkO+m2NRN9910AGBf6w9RtDjt2u5PdR45hMOgQVRC6JISkglRkWabh/foRfzWFQsGyotnMS9BxuKqaZze+QKw7nj8/m8iXPldKfp2BuEXTMc6aSs3O9YTdO5cnX/sup979FIkhRSxdqsNuF3j6m1ZUUeOttQE+egTEx1Wg1SVS4lDR2/QeQ04LybvrSL/5k7hlJ25bL2anjZOVFaRc9x3q6uDUKSU6k56zJUGEhXqxO1dx3fdaGBjoIyw3i4H9R5GEejw9Lny6KJLCM6g8cBaf10f+yiLUSjVvnvC/VQsI5CXPodc+RLu1n7KuanSDIovXLiYx2/8AT8ldQVz6XEp2PUN20Y3kFt2IdaAHt9uJgIAkSyBL3JTjZmfbSW5JXTDhcb5Y2kpeVBBVTU209YUTZQrG4fEiSDIG/VjB4pN9ALiOHkLMyKDX2T+yzu61c7LzJCsSV4zbh8flRK0e21dv1Xmuf2y8GKirK0YTljwiPCS3g43lR9ErlTww52Z/FA3QZt1LksLEruZtzIydgqWmkphGDTbL31Flz0YMV9O5/yizHvo4siSxZ8fPmDzWZhSvawi3d9QzU6FSg0oPJ56FJY9D0SOX2Xo8Pp+X7aff5eYpd1BecpLqGiXtTck88cI5UuKjuPGTvaRIu5mWmIWYsIr2zn00Nzfz3W8uoqNZizE0GEkMAsmDLIBCoUSS/c6mer0Xu12FX2xKTM2wceBMLFpXE8lDNh772Bl++WwhPp+MQiHg88n4vF70Opni6lTau9SIeNFrZUSFiEKlYP9+GctQKD4PZGfDs+vL6TrcQtbshXz1K37H1lOnYGloKQfPy3hbU4C0keO95RZ47TW4b1MhCVmwcSM0Nngxa2WMZhOVh8+SPS8fnVGFzjhxleL4qXm8d+hlFkwdvWY3htWSPK1wwvayLNNZ10pH7XlSpudh6XHgtntpamqivaMDj9NL7uxk9EFja+w4XVV4vSeJj1uEIIz1eXJadSjlLNRaBSqNErVOgUqtQBD/8ywzYdowQh3HWZ73JUIMyVR2HWPb+RfotDaxIv1+EkL9v0W708nGnfuJjDVx86plaNUajpeeZfqUHARBQBAEgpODOf/XMlLum4JapyRWjmLtbT189TMGPMaFNNZ343VqCGkOwdfeymvVDmIqX6Gi6xBSUwM3yAM8cN9vue7ObyG1N4LkRVDkAwHx8Z9AQHxcBc+UPoNCnURZZwLW/iCO2wU++/yjhD72d0ISpuGVFfxy3nI+8YCE1SridoPQYkKWoatDzZQuC7/743x+8sNGTp5+A31EF/NS8hH6ekjNnUlQkj9Dal9TN6V7S7hlyS0j+/7Gxp/T6JaIC4pGECS+v/Qh3hVP0lRRRmr2qPVArTEwe+0XOXPgb4RGNpGYtWTCYzla1znh8v0tfRg1ShbFh9FyRuBsewcDdY2oRBFBEHB43NDlJTwhAacso7BH4ti+FVVaKuUl+3A4I3h9/+soFAoOdh/kpuk3kWvOHbcfr9uJWnOJkPGM9aV4qfYwexv3coO1GZ05iRcO1lIlZjJfp2VF9mz0urEPqlnkYW/rpraxgU9cfy+VO15h1iM/uqh/D6LyNOAP+w2bwHfkUmRJYsvhJ1k9/1tjV0RNoc+bgLemE+gESvA2NRAaeiFi6aKpHhlqpAYc/c9gaWqgcqiHvLh5PPRwEEp9Nrm5IjGJcP10Jydr2ok1m1HF3M0eSy+tR48TpNVwy5wb+eWPNvHN78/BbQvC65Ho71NgMDpQBw/R3RwOKIaFhx9BkKlqi8Y55CFyyMtT/3s3bf2h6LU+vD6BdR9z8PobHhQaEzEhfYimJLw1AAo8XtBoJCxDMqBg1w4BUQSvV+a9Q1289I35+GSRMLPfibWyEm6pfJC8xWEM9FZx/+Jy/vK/TWjnrUGp9Bfha9pRwrvlKygvh6/e8Srbj2RR1mTiS4+HsWXf5b8HlVKNeIkYaKo3sm7dxBYGQRAQRBGtUYMpQocpwn+tJeT4H1hul4cTe8rJn37pNZBOb+9Umpv3YAxKID3NP+1ZenQrQaEKzHE63E4vTpuHoV4HHpcPGb9T7ee/ZmRwSCA91YfVJtB4btSX61KrjCAIqLQKUIs4TUqMGhVGhYhGFD4000yLpv6U9vY3CTGsIztyNtmRs3F5bLxZ+guiuoo41+MFi4BWl8ndhdkj283Kyx/TT3hBBGcb+nj6p/uYmRtJXLUKhQP+7+chhEYmEWLsJi7dTIKiBlHfjkpy0B/VQEh2CI2tBpZPu4eqir28duwYMuDz+ViWLvLPyRwS4N9NQHxcAVmWyQzNJCkomRdO7qdSNFOwdAXv1YcQ8eZ2chJS2bwviNMN+Ug+H2633+ztn3/333j2nsjjloRWTtQ+Q1xCKsHVCWTNuR1Zkjn9t3NMX+cvTBeWGIEow7l9pzgwaMcliXxy5sOkx0VcMiaIjEugqfo8iRmZY9YVLHyQU3ueIzqpCPUE9VT0Sg12jxW9amyCtOOtFr46nE9EKYqsyR3vdFb9972kTolCoVYC8YC/EF5+WiaDb++iaN4CNEoNvuM+liVNbFvwuByoNWOdxYQQE1tefI72+dlEao1UDdTwl2VjH/pPlu8nM6GAbp8Xa38zNZYmJFlCEEQsHpE1i+YTplvMX956m/juQRo2HSVhdREKpYKhnh50oSETjmcyjpe+xNJpn0CtHnsO7T19SGkriFwy6q9S9fo+Ym8Zn08E4EFZpnb9ehx2B7d/+ae89RZkZTbx0190s35DFIcOwcyCLArzMth3ohyzOpS5s6YDcLC5j2cra2k/WYg5Wc/nFu3mpQNZHDsdT/egAleQgDzyvBoVPVqljyyxAmtIJkvXqrEfbSd9qkhYRDiv/93L2++qcHk0uGwCxf2JY1KwyIDTNfqw9/lkfH4jF29/ZwZ9Fj1arUx7O8TF+cXF4K5aNCku6t1ZPPkkeFt3Y9/yEuqVD3DPPdBWPQ2XZCfMrOP6zz1I/EL47ncl2tsG2bHvFCsXT5/0e/j11u8Qoxmb8E5nMGKzDwITlzXYu7mZzcdn8eK88evUGhVOm3vC7cLCEhkYiGdgoAbwiw9zTCLN9VuQJUiZMn/cNm+9BdNmjnWqTcqdvHKx5JNwObx8c2cVn1yYggUZq9eHW5rYP+lSnGVlLBR9XJA1glqDaDSgMBoRjUbEoCBEtfpK3VwWpdKA2byYwaEygoP89yaNysBd+V9nfcleoluj0OjMVBzv5tX+Cu67c/JQpkXXZ/GD70ew7SVIM3o53xRE4eIKVj64kf7jN9HW5sXcc4IDWbezKQEej7+N1NhQFlSsxNqUyeO3/B6zXodCEBjy+qi0OQPi4z+EgPi4Am939NBosVIxWEp+0AkK1elo1FqeV5czN2YJ5RY3u48FIzuUOJ1ja3tczPatkbz79k8AqG3aSEOjheMnO0jKDeHAthrS8iI5U9mD1+WlpdvFnTcVYDYZJn0bypk1h/ee/eM48QEwdf49nN7zPIk5c4lKGGueTg8Kp3KgnRkRGSPLLC4PWpV/7D6vb1zV3AuEFCQyUNOCOSd5zPLzh8vImT4VjdLvB2LSmni35F2CNEGoFCoWZi4cSSgk9A2gUo31F7lx3cfY9tKPWW1cQr9WR78rjUuRZDjTe57W7r0sjFvA2oRZI+G+x6t24261c7K6n3uuu47gYCOu7n5Kn91KSGoU1vZ+qu1OLLtKALA6XbxzeDMCAu2ONpQRvnH7C687gNFhGbe8s/oUBUu+N2aZrbF9wvMF4HU6cfUPMOXTnwagpgby8924HBKzZo1GgHT0WjhUWs9jd/rPrSzL7Grs4/sLinhyO6Tc6OJ4bC5FUWH0ODXI7U4cVg9BUV3YLCbyMmyotH3sOpaFoJA4ZUmje1DLb9ZnERZpxWnp5HPT9/Ke4W4MRpHcHAv7DwePiOTJGV3f0mNEoQCthuHU+mDt9/HwHwtpO93PzOv87Yx3fg7H3nf5+5feI9kby1++cZDb/3ATNU3xLFrqo7bW7yC7aEEsHYNNk+65dcMvuM0QRY26l11H/sD//fgePC4z2dkr6eypwOZIwKAbP12jNRjwuj34A6BHueCT8eD44CNqarczNNhGfPx80tJGpwtjk3KITcqh6vQOakuPkJY3thBSTQ0UDv/ELv4+J2PQ6eWbh2p4fGEaSeZrj9j4rkqPcaAL8YIztWeApJxsIgFPRwdSTS2ye6y4kj1ubIePEPE/X0RhvHxW5gvodInU1j01Ij4AFKjJdKgIm2YkISOdW2/MoLq/mp+f+DmpplTuyLxjTB+//p+XeXH7WpIjGrjuczvZ//oc5OZ5PPpAASf3dbB+SygtbfDzH0aSM+MWXomNw37+ddTqOXw2/zPEm5KJNIyeI6NCxOod/1sN8NEkID4uw76+IQpNwdwRc7PfgWpgChU956hteplV5vmktAcTXHWajS1JOOwC8qSZQgXsdhXr1sFfn3XT21PPUFMed92WxR82V+I8348yRMvyeYkoFAIK1XhBMRHRKWnUlZ8jNWfs9IZKbaRo9eeoLtlMV9NfmDpv3Uh5+ZK+Fu5IGitIni5p4vMz/W+XHp8X1QTp4N1WO33F9aTcOv51UpIk9GGjN7WVU1fSb+vHJ/nYde4gTx16B71aB4KAz23B+/a2kXMlCAKyLGMU8ynd+B5rPvEVpobEcW7/epRGI1kz/A+Cb+UuAmCf6zgzYuaMEWV9rT6CTR7m3z4qqFRx4Ux9ZDWePgtJqwpxbmlg5nK/ZWceo2nzt+w7wNr8CSrT5n9q/DLAHXaO8v5uFiaM5vuIWZhH845iElbOHNNWlmWqXnyR7IfWoRyOfEpPh307Q3A5Oim+KPnt7mNlfOuRG0aO69fHG3goP35km1On1ETOD2HXMRVuBTz1vIvVU+J58Q/9HNnRw6OzdrJlh5qYj2Xy4otanv+zhT3bGshOt7JpVwz9Lh/Pt0WQmCTwyB0H+PaTsyY8vvGMFdI+HwxZQacT6O6BV347SLRKyeOfL2Fvdx5lQ/5oKt2SWznzcg2J+TaK9+wjfmk+x3+XhNej4pabRE6dAlH0EqxTj4iCP/6sHp99kMEjr6IwJ6NLm0dY7nwSgTf+7kAfdJAvfb+SrRunExMxld2H3kGj1bNy/h1U1LTz8QdFjIZowvVhqIJEOjptRITrUFxyPQeFGmiu7SAhbTTya2ionenT1016FlRqLZ3te0m7pAqj/7uB229nzPc5GaIgMFWv/UDCA8AcG83Ci3IDSR4P546doK5vALUoMm3xfLRBYwXZ0N69RH3j6wjXUCXbaq0iMmL1mGVlR/aQWTgDvdE8sizWGEuGMY0USzS9ZXV0nW0kYmoyYVOTsFv6kTUh1FkyKf1FDL0DBlQqFd/6rpOFK+Jp7zIgI7E/8lbmG5IZrH2dQXcNIXV9XDf10/R5+sfsXxAErj5VYYAPOwHxMQk9LjfrN/6VeLcGWVTSGDIDrVZDXEQ+b3c+h4qXuFueypDxVsrtCchcLjGX/wbe19FI2eESptz9MEFGI68eayQ7JYTl12VfZtvJmb1iNSd278Tn9ZCRP23c+owZ1zHU10H5sdfJnXMfkiRRY7PwyfJWnA4Xq0SJ+kElC1IjMKj8l4LH7UGlGn9ZOHothOQloA4aHykje2Wkiw5fISoIH65qaxKzWTU1hfDg4ZvtrOsmPJaTLfW8fsLFIscgO3/4aQoe+CKVhzfR5rZitQ6wcO5NhBjCcPdVjAiW1tp9aFUhGLwOdENuesp6CJ86Wk1XqVaijDYjSRJckqNjZOzytb1JTc/M5an3/8yCvCLe/dkfiJsyLPwaylEYXMTOGzXN12/dSsLKlSh1o2b4W26BV14O4a57JJIS6nE6lbz39HosiihqyusoFnW0WJwUxYWQHKwd2ea11wSOPhJFbLKHFoeL1VP8UUX33C/z+us6vvjeXYQ6GhF9nXSfstLZn8HN95u4+WbYsxZKT+hRR1kQbJU88cspWIY++E/f5xNwuSRUKoHDnSriC7x0pMSis9TS063E4w6mZtMmsqbM4NBZA47ZhWT2Z5OYCLfc2smcBUPo005yfMciHA4LXqcNR1sLvdv/TNC0Gwlb+Ri6iLHG9bpGHffduZIFs1ailAZ4snSA0OBBSs6fpLXNQ0ftXKZPN7N67S42vx1JbWsQxae7cbklfvW/UWBXkJHkxeFVkhwdzdFt50j4bPRFx2Qfs7+yngpqrb24fF5Wxc2gtWYvKt98jr+3d7iFjK/CypqHlvPaa3qWL/c75V6Jys4h+v+Bt/dLbaqiSkXeAv8Lgc3j5fCGTSy7fWwlbEVQEN6+flRR4/NvTMbAwAkSEh4a+bulppaeZh85s0LGtDOoDGQcDSYkxYjDZSfjrqWsKKzFJvczveiT9PQNEBGu4Z23wlm1Uk1nL/T2aqg975+yVSoVrLruq5SfeZ6IJBH6u/FF5BFa/w79XbV4V01DqRwVTW5JumxJiwAfHQLiYxLCLXU8FT8Eix/j/On9qKMiWV9px9vnY5njTtYYbVSec5Kl24boGD8XPBFh0cnkL04e+VsGkswTZ1C9WoqWreDM4QMc2rQB8DtlAUwpmk1ETCxBYdF4h82woijy2YwF7O2qR4xIZPP2PQTXN7HntJu65CSi4sNZmFGEUjn+spA8PhQTiJKms7WodEr0wRMfh9sroVNf+TKLN5mp6hzipeef4uFv/4mOfXtR6sMpyJ6LXhfEoWObGRjsIUQZy5nDv8fl7CE+ZTWdxWdY+Cn/TbLzdBeNW+pBIeBospL9ian0VfZhOd9P2MQZ7FEfK+NoTxlqlZapq+/DNthDV1MVWTPHFvqzDHTSWXWatJnL0am0WNttFKxcidfjJmtOHj7PfCpe/tsY8VF1zsfrr6eNcYxUKuHd9UoajtQx1NmBLEkoVckUxCZwsKaJ+MwU7h2uu9Pd10pT1V4EpZYnfhKNq7OCbnsYan0yMANnRwuNR0rZsHkBA3V1DGx8nfRv/ZT2w+fQtJzlcF0Ct98eyl13S5xzHOLI+0v57RNv8eOnrptoZvCK+EWf/7PXKxIa4eT6pcGUnIIl14fwi1o7LbKLnZu3Mnv2bOabe3l7YyKVpV+kcG4w4WZAiuGGVTFUn9BQezqYp57IINd8Gm3UbBIe+NWk+77YwnDuXAghphDmzvwchsHXmb7iHn/6+OVwy43LSUmEp56CG1YH+X1sUi08/nU7e49FsXeHB7XKTbpq7AWhUYeOfK4b7KTC0smdaUvw+Ly8XncAnb6V21eM+jHZ2vtoaDtK5ZZTvPXW1f3+AXrarHTbXJT1WplqvropkKvFoFKi1unwuFyoNKNTm/rCQiwbNxG0cgWiRnOZHkZRa/xCRZa8DJX+GV1fBdkROroPK/H4o6AZqu1BFRaLUqcmfkkBRw8f49nPbGHq1Cn8/hUzP/gBdLap6ev2Mme2GqdztH+l0u+s6/VC/gwlP/x2H39+6Qc8/xcXe/7+Q15661GefXwH33njJhKmf2l4KwGXLNBtj+fBpA/2whbgw0NAfExGZDakLILeGkzRyXScP8IypcjQUANbBInqChcDch6h4QqClTZsXLuIuGdmAt/fcI7v3pCDRjXJ03FCxj45CuaNnTaQJImdr7/CqnsfAMDjHn2rU8guTCo1y2PMGIvm48wqhDN7yVAco747ht11kJs4tiw8gM/tHSc+PA43Ax195K8qmnSkXklGOUlV34uJCgrm2eUr2LK/GLUxGHd3N8vXfWFk/fJFd4zbxtrVixA16nQYNS0Spvk/tx1q4/wrFeCRyFyXS+OO8RVnARKS88m6YxHWwV5ObnkRZ08n5uRsjr7zNLLPS+zMhfTVV+Hq6yEmfzb7Xn6SKQ0S1uBB1E4d9oZ2bKn9GCJD8Xk8+HxeBEHA5/YgeTzIskx/j4Xuzj7CwkMoK6lGkAUUtcXMe+yzY4prDemqMBmH3/JkmaaKXRTOfxBZkujtbyU8Yy6IIl3Fx2nYugNJFsi8xV+gzli1AdO99wIQMy+Xz8+Cm1dYmJPbx5QsGaMygT/WHuO1eg02Yx+iQ88Emd+viCiCQgGGIC9xWXWkJNXy7J9nsmSOksjwNjJkPdPiwuiq6yUk3MRrL3vQBhvQBo2mW1++HGRXMEFBsG+/iV98/Qjn+6YCk6fLvhC2e7GFobLhDDuEWPacbqRdH8zgyRBuv10Ymf5oqulj1xaZgmkiGflR2GU4dEiFKT2EQyVwsrRy2G1Txul0U9vgd9gwKdXckep3ILY4XLQ0K5gf//kx4zHEhJH7mes49t4ebINWDMF+IeG02JAdTnTRo1MT7Z02eq0uDp3pYM3seJbPiuP+fZW8tWJ8NNiVuNL7/rQlC9j28muEqtVETJ9GbFoyRp2O4DWrsWx4f0xiv8vh8fTj6a/BevKX6HPXoc+8C7NurOO7p6oYZ/EBgu7/EqVllQyKSkqEbG5dFc4rO5+muGIOoiIfj0fAe4nh8eK/e/v0vPj696mqEsnM1vHEJ2I4cCYC1VAz9057hP7+0yTFzCQlZRken4e9LfuAgPj4qBMQH5cjOg/q9hI15WYio5MpfeFVgnSpJFj76Eo8wGm5gy3mmURHSbSP9028IgqFyKOL03juYB2PLc248gZXyb63N9Ld2MWu59/F+95f0N0EPZX5hGcvQJadIPgd8VbkxHC6eYCjDUnM8yZj6u5DvTQNn3X8a7HkdKPUj85Ty7LMsS0HmHPjxFEeF/BJMsqrqF753ms7cDndPPrIbf7+r2CaliSJ9j1lpN42QUgDEDs/Fmlu9FW/4RuDzcy99TPjllfueRdxyMacOx4DIClzJr0vbsA8zy/Q4qR4mneW0OXykLB4NQee+CE/2fQQA4MKIkLjkEN6aanvQ6fX0lzXwaLVRXg9Pnb191H88t+Z9eD9I/tSKkSKm9rosTtQlL9KYtRUZJ8XQaEk3Jww0i5y5nh/DXfnAMEpebgGrCAKSF6Zv/y4kZYzrTRZrITnT2FpzDQed1uJUw/gCOqlw3L1ZvgLyDIgS8hYiXQMUTTrJnbs9K+reP0lvF0Wmk9mkXHDakLjw3lz50uYgyIQ9u2nIGEhf75dBknidzumYlp6EJVwJ2vvnE3lU91cTnxcCNu9mNJ6WJ5ooDAjiad8TfzqSzLrp9tIStLgGZDZt76NitrpmIfz5V3sk2HOSMLQ72FmURwGnQoYH7HhkyS+v/cwv1i1CL16YouBUqlCpVZx7P/WExwTjMaoQdRqsO08i1ojcsIQQ5hJS7hRwydvyR4RmzfEhXHP3nJeXpR9Vb+Pq8Wo13PDIw+xb8NmRIed4vXvEzVjBolJCXQJIiFX2Y/cV4u9rZmQ5X+cdIpDlTUTWVCy4ZXnSJi+kFVzCmmucvK177lJjruXgc4QZMmN1+vPPzMZdjvs2yeA4PcE++pvP8lAn8ADv7yLl96KgJw7OHjid6SkLEOlUKFTTh5NFOCjQ0B8XA5dKKQuhertSJKIceAEcnUnK+YtwFXeRFF6Hjsrqvhjz/VX0dn4H3BfVxt1B95jbmgEZ/eeAeB31XtoUpp5bM515EWlYdJq6bEPkRkee9m+LuDzSdQ0nkd22XnZ7mCZJxbRdRN9LX8lxredYLUBp3oR4Bc70xJC6MzJJizf339vRQMKjb9/SZJoqd3LYH8Nnd12hkLWojvTig+wtrWxZkEhygmmYi5m0OEe5/AHUFNaTfX5ZkyhwSgUIm5J5q5119Pb1kDpH3+HPj+XyrpTBLlEdILGb3VRKlEolAhKJZ27qojMT0XqdSKpvQgKEUEUEBQCKPy5HgTF8N+X5fLqJHvp+DdFX/+o0hRFkaRVM+lp7KR82xEOsYyUfBuf+3gpv/5xEH1COqkJcTj6nKTP9fsxqDUiq+5aQfWbY2tkZKUmE6lqwSmIGDJXoinfh6+zCWVs6mWGL+N1OOh162l9eTeCQkSpVSOIAiGZ8Ux/JJuZWjU7/3iMN0+3ERJkoINgDBoBUfAhySJXfp/2o1YLREeDzSaQqOrAIGo59tQXmPnZn9Fzdh/9DVWEZ+Qhu0wYw4M4VXGcGalFpKVmc6ytgdBhKw1AtmOQw29n0Lbx15xofQBfRwPW1zeDDKLRhGb2ChQRcZcdT63DQ6HJ71wZZarnz6+EEDLUQqTaSHr6Yo7sEjFu7ab0tInly9VjfDJmT4/j4P4G3G7vsPgYS3VjIy+fq+bx6fmTCg8AfYiB+tN15D+ykorX9pO8uhBduF9E2bsHOPL+UdZct2bcduumxLAoNoQZW09zZu30f7oPw+Kb/L5VKflTOXX4GGW1dZxKSOOqXNnbThMtpOMO1V52XIPWfnYNdrDs5tsxGf3TVkatFpXg9k9Nqlx4vUpEQR4O5568L1kWQPb/Gjs7tahVEvuOmPjYHd28vDVqTFtREGkZaiE+KP5qjibAh5SA+LgSuhDIWoO7rAajKpUB2cjhbRailYnYvInEJeUy5AhCqXDh9am50o3c65U4VdrFQF050RFDLL597Nv2/ZKRuYuW860tb/Ni6ZuYDF4ElOSGzGV2dxmG4ByiPApg4gyPb+w8SrnLQGxwA5rcEHa4ZpA9sJ70yDxy0x/G63Wx8ewRdM0vI2hDQWPE0dzLYI8/UsPV3k6XuZj2IT0gERk/m8SMZXS2FLM0IhaTxn+zPx+n48WGJu7Wq4g0Tj7lZNSOvbF7nS5+9/v1TEmJZO3ty+huaEGSZGYtmo61r5Oz779AxH13IqqVuD0uyja+zdTZNyD7vEheL7LPi+yT0ISqEHvacHQ2I3sl8MnDb+WABPYTpehmLkYR7B+ve8hNzfZG/6v7RTdUbUMZ1tfPgiQh+3zDYkUEWQJRwUghFH9+bqwdzbT0htKz9zQgI8v+/ZoigtEX9tJXlc3MKRYMGgf3PlDE7//kpXF3I+owHeeKz6GYYmRKeirm0BB0YQaad50iYbk/z4WoUmFOvRBFk4Szt+Wy15LP4eTMy+sxmo3E3ngXxoy8Sdv+9lNFvPfyPm7zOCiPC2HRYhO/+pOCocGQK4bb6rROkHxEGIZQCJF4HW6soZkEZSiYvu7bNO9+FYVKzZwvPsVg/VkcThN1O49Q76rkttv9IcbmtFzqSvaQVuj3m7hjXTBvbZ3NQ3+YQUjXXmyiCePd/mk2qa8d14nd+Pp7h78rf7E84KLvTyZC2cLJwps5Xn+IWeY40kPT8XoLuPHGLiyWPgoLIzDHSTz+8bNkL5427rgEEax2D6Em3XDXMjuaejneY0UjyJjqbZyt301daChqvd9a2N3YSnhCLHnLCjGGhDBl/nSq3tnIifXvED1jIdbm7hHxoY8IYUpsEI0tPSTFh4/bf6pJx4sz0/lzeRufzr280Pqg3LrnOyyOX4JPlgmfwFl8QhoOoJr3eS4XG3Pk/G529AyxOCUbpUZH7YCd4g4Lm08E8blHjrF4wXHePGyh7omf4vVcybIz9vqTJXC5FDhDw7Fq41i3Dh55bHT9vNh5lPWU0e3oZnrk5DliAny4CYiPyyA5vPQfLketU8HJlzjb1EiVNoZ+wyAF0Y102G6kaMdfiDbMpc1lIEhrZchp5NIfk0oFN9/q5Iab9vD3L5Yx/5ufIrLbR+K8G5BlGamrGW/9GbwdbZTVKtCJpdwXNoXf1FZwx7TZ+KxuEoUYIvvfI8yUR4XkpfhABS1tPTg8MlarG028Bm10EM+cGcQUM50SsYAk6QCLveXkR7bQbHyI7a2l6JRqFnp6mTP/QV55ZTV33bUeX/NWNEviEUQRb3knOQkfQx0UOuYYbF43BtXotEtmeBipoSbeqazF6vWyKNJMesxwXZfeRrrL9qEJi8M3JFG8930iYhJIzMjn2N+fZnHRDAabDnP0rXIkrwdJhPoSFYJKyaJPfheFOOr/0h+xm7hFl5/amYihnbsIXpmKwuC/4U6cjgqcqmy0S0eL+MkuFyAhaCa+URskH873fkry4oIREeN0DlFa+gIFBR9nzhw921/fw4KiEPadlfDoPIizVbSX1zH/vlX02wYoqzmPzeoEtYS8dfOI+LgUVe583Ptfw91ejuaOJ1BExjFYU0VfdQOSLCKKMvkfuxml7soPlXP7jhGpdfLJpV2sa5jFcxsHkVwqZPnyDwZRlDGb4bFHG+nrTeHbK4t56hkvJwZT+MI9dVSf1ZOz5uMjb8ghU+YSAsRMz6H0uVLKTp8kJ2866fkL2f+3J0mdsRRBEEamUY4cL0WnjqFuxy4az4eRkJaCGBaDbvX9k45JlmXa9z9LRsgs5iePdfZcvx6mT4/kU5/aw7N/CKa+PJ7o9InfkGfOjqespIOW5kGCQrX8pr2X6xPC+Oa0RJQKkZ0WkVCzlsL80TdvSZJwOz1UHDxDZ10N8+64jqzbbiA0PoKB1hZ6dYv42rrR7KszV8/j7Ze2kvjAmgmtCNMjg3m/oeey38E/wuqkFSyOySPXFMP/Ff8fJH/t8hvIMvRP7B91gSMn/4RRH8XspAy2nj/M5sp60kJzyYkw4g61srUiHzXtrH/2QeBC4cNrp69Hg9UqUnzcRUvzXNa/24Yx2G+hnRo+lf0t+5FkaVwG3AAfDQLi4zI4qnupqD6Myn2O1JlLcGdnYba3Mc+kIHxjKz/U9pOUez+LWl/h73sexeY2otXIeDywYcNuCmZlYDaG895vf0pw+kzMqlAGgmXsu07Q19bFqe1vEKPpJW+xGTk2F2tSDjM3/AWpohGFJPLYzFwMrij0wUaiImPRZT2NqDeye+th4rETOzWTuHADSRH6kbnku4ZTTbi8PrpdWfhy7CRExlF00bzyWUczlSXfw5uQxfc23s0tfUZiNlSjSinA1tlEcNr4N2iHz4NSHHsTUSoU3JWbSWlHF3vPV9NcWY6+YSeRydlE5C7GbeliVkQPSfmLaaws4dTm5wjx1WPtUbP4Y9+4KlOzSv/BIgLcTQ0jwuNaEK4QDSCICkSVboz1xGrtJiIiD7XawK23wu+eSeL+b6Qze0YckXEKstPiyE7zJ2uKMIWzuDCc2tKDtJeeIPeb/zPpvhTmWHS3fhlP+T4cW5+ltl5F1MolJK9dPek2kzHQ0YOnqZkht4cvfP0kgvgUe398C2KGh30bH6B+IAjp4nhpBAQBMjIE5szR8dXHc7nnHrjtV7OJMwwRnmggZ6GZ/vZOTrx/iNCYcDwuF7LkJaUgE70piJnmWQQlJnDyyEFEQSC4Q4ershFHRBBbTp8jJiyUFquDB5bNwNXUhN3t4cjmbejNZqbPmz3hcVgbTtFTtZXIgtvQR2eNW38h6Vdy8lJWLS2lvk5JSJx5gp5Ao1JSODser0/ijVfPcuv8OK5LGXWqXLE0iS076mjpshEf6bfuiaKIVq9h+qpZuBz57H7tu0QmpNLTUkuEJoXu3TuA0UgpQRAI0qlwOVxo9eN9FY61D6C/imiwD8q8yGxO9jYwNSSW2zNu50THCYqiJ3cQ93/pqyZc5XZa2H3818zMuYfw8CyC+/rY4/Lws1Vr/Llavg0vPQdr1g7ypwNr6O8Nwuu5mtwiE099+rxw6KAHSVJgOTqN9du+x8fu/OlV9Bfgo0BAfExCc5+dv2yvJjo0l3KFkSybGo0YRWdNFdOUTbQh88UIB/vDPCSv7KXa1Y+1zceMHDcERXLd9aNZElesWUnJjjKmf/4GeqLj6T9XiyksBMNQPZq8PA50CpS1DNKodzF4012IKiUPijqWrB2f6RMgNyaJ1QWXN9NqlArilWYwjL/xhscvo7pbx0NF431V6ut/j2KCOW6NYvJLZX9jC48t8r+BytJimou30lZ+jMwldxE27EebVbgEWMK5o79l9pzPXnbsF7AO9aG8TAXey2FceDWl4z4YTuvAyGdJkjhT+y6LC/3REKVtb/HpJ97jnjnPI4oTv/G1Dfbx4sY3WBY+nzOv7kCpUCD4vMQtXUhy7vjv3CYkc641n4KH5mBMHh+JdDE+n5fa4mr6OwaGiwrKzFhbSE+LRHzuDA6fKeeOwmkkxGzl7JGznNoyQHxyCW39uTjqIwABUZTQamQizS72vL4HqnsZfDOCP93ko+foMawzbmPmJ2YAGmLSE7EPWlFplJjjzIhKJSc27kOt12E7XcOKW2djXjActjlvAbYj53ivqplVM1Ix6jQsneEXurOHfRS2HjiIva8fae4sxEvEadv+51AYjCSvfnzS4784JLemPQ+vpwmYWHxc4NiWGm64Lotg8/hrLS3JRHe/Y0R8XIxaqyExbz7peddz910+WurbSI0axOOuRZZTEQSBs8dK8SmUEwqPDruLl+q6+P38q0sq+EHY3naG/8lZi9s1REpwMg2DDRxrOUpRZBG2jgHaHD1IkkRsbCwqlQqprwFjZxlkjhUgHW3F/OnUH1iW9yAKYySnu07jdRkJUYi888KjKOSb6GjLIX9VKM5uL4IyEqv9H00JJuB0KREFCckHuaGj34HVbcUn+QJWj48wAfExCVUdQ6y8JZuDW8+RpbeSnJ2L3edEax/CMpjDzM98hfJTJSyzDNDsCGf3ZhXte94kcsp0jHFGQIOztZ7i43UkGmX0MWm8sKGaM0N2+lWh7MpSsyYhCSUQrVYyPTKYuxNCiA3RIoqi3z9hAnw+6R92Tos1hFHWpcLi7MWkHX9jvtb+TRdVvBVEkcRZ12HrbqF67xv+NylRJGPh7dc8zraTB5BcTrb/9mtMV6WN89eQHXaCVqxAlz+2oJWnsx9VfMSl3fm3kWV6//ouol4HAvS2n8Cck0lYVMKYdlWnDuAa8Bfhi86cSWRc8si6sOk30n7kTWLm3smzZ8pRmwrZ0HSM2REZ9NmamJPxaURx8p9WXV8X9VlmFt9615hzfeTpv3Hq/V3c+s3R7KpdZ+o48tdj3PjU3WPCcifC4/Hw7s/fZ9G9c8ic7Y/esFtsnNx0grBYA7NvmkNHqIeE4emxWTcJvPO3QpSiSLBNRhkkoFYLzJ/dTFGhhZOnwhjwgbe5HH3oPJAh5J5bGbKOnZJLmzG2DtDi+28AoFY1drpDEASM86ZiP1BDVFzshI7Iix5+kGPvvj/Oc6p++4+JyL4ZY+Lkfi0wPiRXQESWfSMZfi/FuW8P0T3naTgbhow/A2l0YibhSTn093QgKgzITs+E29ae20ls4kzef19FTq6K9e+l8sufNfL+681s+9lfKA2ZSWpyKLfftnTC7Z883cSv51y5yOHFTGQjkCWJurrjdPa1oRVBFPy+SgKQYW/lRMmfsDkH8Ho8eMkhMiwdt3OIIccQSpdM0rQMent76evrw3NmE5k3f3VM/2dLX8EtS/zg+ud5r/o9ni97ninmKdT1NbH7ZzfxZv91zJsTy5DdzeypfRzu1FPX4uGf9XiRZAEZHzUDBi5MUJ7uPs2ShCX/lP4D/HsIiI+LcDc14W5ooN4mU9ziIl7oJdTZyzllBytqE2iVT3GkzobZ28DWbz7I3sQHUAaFEa4pYPfWchzSVM7tGaDFexK7To9Lo6AlKJSpbjU6k0CGQc26ohimxgWjvaa8HqO4PD40yn9c7S9LWsH22re5LuPOq2o/4L02QWKIiMcQFol9oBvrwPg5bafXyamuUxRGFaJWTFwIK3OJP1Pjn/7wCUKnRVI097ZxbVq/9nWCr1uLqNcjaLUMHK7B2WjBO20e2l4H+kveZvvf2o5xSRHaNL/YqHzVg6+llvqT2zGYE0goWIJBq8bZ10LB8nvxuhwc2/gX2i/EawKCz0V7XSVPNkXwPysKSDFPRZIkDnSUcdgZx/KI+fgk3xjflYuxe2zckrl6nMib+5kHefNnz+GwO9HptXScaaJ282lu/s29lznTo6hUKtJmJKLUjJ5PvcnA3NtG/SJcDifvHzjA6bpjxCXF8rGPu2ivjyR7/m9pPnUd6zcuxupMpr0XTGaYMiMBZtwwZj/Nm+uvajyh6RHUba4n9bqUMcvvn5HIuyXN3FGUNG6b8ydOEp2XO+7cRE+7n/7aPVcUH5eG5A422hnqqiQ4auKcGrLTSdq6R0f+9np91NdV0XrgPZQeF6dK6oiImkFf2DzCYkJG2nXVtODoKSUkd8WY+i4r1iRxpiyW2Y/EE2HpwX5+G2feOYlHbSJ91nX85HwHkhhMlFbFsuiQSXPgXEg5P1Hl3guUtFs4f2o7WREC0SmzmJc+Z1ybggvnoaaYvpL3cAlhhAYFo00PoXtrFSkzMlEqlURF+QVpW3PUiMD3eVzsPfZLMlOWkxDnnwa7OWM0e+q63+wmPMHL/32hhKfeTKelOYHGdhN6sQdR+meFww7LLUGgo3u0DpBKVOGVvKgUV58yPsCHi4D4uAh7SQmdP/1f1LLMjTY7dQmzCZnWT4oxkbb3yhCcHSSIy8EzQFi4mvuDNbTPSaNjyMnBQQdaWWBqip5744NJijSgu0IY6mWZJALU5ZH+KeJDKYqkh8+ipP0wM2JGc2X4JMeE7UOV154S09bfTcbiseKm1lLL/tNPA5AXnseXdn2PNMMsrs+cR1ZE9ETdsO7jv+bNt344ofgI/8yn8Q0MINkd+Kw9NLX1M/Xb96HXamk/1k633QMIuLo6UbVVEXPjohHhAYAgkj08JWTp66b5xCbsTifmSP+0llKjY/7tnxu33/zlUHGigRSz3wogiiLZ+iR+29HBi+ufxt7TzKc+/iNEQRzzIN1efZp+h4278xdNeKzLP3Yj+371LGu+83kisiJp2q3E7faCFyRZQpL8lVGR/cX2ZJ+ELPmnfwY6e+isbyMuI4K+8k5/hJAkIXu9/m0kifl6DRs79pKQEkVceASNue9wfuejnD3yIFkJVhbMtPDKO2GTfaXXRFh2GEONg2OW/eJIJV7ZSU6EguOdZWMuc4/HQ8+OTcybn01XewmCSglKBYJSQVv3m5jzbqKt/R38MZkyMv7wphDTTAyGicORjfHpNB3/G8awLMRLfo9SZwvyJTMDSqWCjMwcyMzB3l5HRLCJ8JmrKN9/kspDg0RoYlB4NCgUbtIX30PbqfcwKxPYvzWI22/PpLgYRIWK0IhkCiOSGXKfQ4hZii4olsoTO/hUbCwne5uIVCkRbPDOCQe3Fq7irdpuWu0uZCDbGc4vv62noWHi8zro8vLb4gZyooLIp4ecovH5aS7F0XyOoNgckhesoru0lpZDZ1Bp1QRHhk7Yvqn5CJvOPEtaWySVh14h5NEUgkIiaW8qpqGvllpjJH3dceSld2ITHKSbT7PXG0ZLWxRqTThu79WKgosimSZAqfTi8ykQZYF8x2gBxwhdBJ32zkC47UeYgPgYxtY1QOn+dgYz70KWJBBFkpdMRaVTIQz1k/ytJWgixk5RVL2+j7U5UZP0+P8Hp8eHRvnBrCaXkhmaxPb6c1hdgxg1/ngQUfz/m8AnzZTGTdNGb5ZnWwf4xMxVfG/XX/nu4odp7K3DoNKTFDH6VqzVGYmJnNj/RZM6+tCRJAlPazev/LWMLbtnEhIax1/+ItH42nbEM0cIXToHTfbkBblNYRGYFl5dBshmiwONaqwIfL/yPL+aOZ1I4zz2Hl7Ptr1/H85dMPqIbajt4rOf/PKk/YbFRZG8aA5lr7yL26KB1BzO7WtFEAR/HhNBQBD9UxiiKIAAosK/XJZUpE5PpnrTcdJnxgznPlEgKhUo1GpEhQK1QsHUMiPRC/0RPknzk1hqfpfczJWItdtxd5YA48XWB8Xd7aBpRyNepw9zjoObpUZ0+aOF/S5MD1x4/Ez55v8QpBT9osrjHcnBnZKejqjTAxfE3HBINAIdHW+TmvrFCfcvKkTipt9Gy6nXicq4Dk2o/2EreVwUb3gBT0EInPg9EjI2bzAxmuH1kg+zIQK1LKFQKMlb6n/zP/zb15EiwzlkiuBmi47sGbfwcD4sWNJDQVEfcck2XA4FJbXt5CZORXAOolUpUSiV5M5dC0DGRTqpuuEMDT0ttNjh87mJ3H23zNN1Hnq7PQgClPa6yLsoDXvfkIs/tjXxlVkp6FQKzjRdnVCMWvoQ/VXHad/0FCFz7uHojn0YEqLxlZ4HQY0s+XOfa6wnocyAoauM6wseQdW4nQaxhxPn/oZapaa35ggJRY+yMn4Oyps1bDnUzeO/S6DutMSgVYfkA6d9YkvmxShFNxJKFFovkkOBT75wTxsrRrw+JQpBRkbgzewHuRD3lmxK5nDb4YD4+AgTEB/DOM/XkLIoi+iVc1AaPljFyX8FFpcH3xUTZ109y5JWs6NuPWvT/T4ZwmUL5F0jEwxTvsSkI8syaoWaTHMq39r4BnNiIwg1wvmu86zMHY0acLvHW2RkWUaS3Ej2IdrPn6K9tJXpa9eibw7mmb+2oXEbqHt2J/G3LUd3/xravvNd1LEx6KdNGzOiD8JLZW08eInTr8fjISnC72uydvnHJtyuMaudvfuPsXTxeBP5BbIXFfHu18uYf898Imdcm08AxCE0DRAzf8GkLSJKz5EWNSrmXD0lRMTlYnPX02vrpvfo3svuweWWgJTLtrmAaamazsY61IM+ZCGKzPkTFxackItmzC4niQXh8rcxlTaYhKL7aDv7HiZLPvqoREoqT5Jx06OERvm/L4/Pw3PV+1mT7RdlkmOAkmPriVKE4HJ7EEWBE3t2oZsZz1mHlkUx4aAS2VDSgl4l8qtf+shMDSM8IgxZlmnsltlVeoTy7kjmug5iUGsod1Rx47R1BBlHM8v2WPuxWvrQ9iuYPz+RsjKBsDA1ah8YDDLV/f1sbeoFYGG0iXBZ5IvzRoW5fA2Xb2jWLDrqTzB44h1m37WQ+obNxMffRXBwNAqF/55n7T8HSj3mZd/3u+k+Np+Yi/o4o0ymIGMJAHfcBr/+VSTVNRauK+xj5+lwPG4HTq8WaRKLhiDIiAK8//tP86mf/5CenghUBgdDNgMyoNN4cDgvEi+ygELlQaESyWnaQZ91NmHGMERBRLrUbBXgI0VAfAyjsvYQunbpVRde+rfhkwm7KDTPK8nYfD7skoTdJ2Hz+f/3yjI6USROqyZSrRwXOXABpaggJSyPUx3FTI+eOWGbD4w0oXvcyKf63n5ChnNUfLxwFc/3nuTeuf7J89ePv47V1cvxlj3MjFtGXEwmG97/JTfd+BUkj5tdb/wCr74cvVpNvy8L52ATf3ru+5R/PQq1yofKZ0CO8ZHxaf+0j9dqxdfdfYnwgKvN7nkxfy1rZVVqOPHG0Ufi+jNlxOivHJnT0tJJXPzlI1YAZn/pTg4VtyM3VnHrLZn/5AyYl34v/r4NKTeQeRWaouzI3iu2cTudlB7eRUhkJHnzRh0uS3uqqR7qQiGA0+dDo1BwIVnboMfDfWmLKKk+QIQpntSYK5cckGWJ4kYLDUPvX7qG/sozZF8UHSQDjqZ62g8dp6a5nMJvzAX801nrDv6ZHxfeM9JW1ASjDU+ktE9g8PAhyppb+dSKZSTGxFBeWsncHH+/2bEmrHYP755oZOe5Vj5WEEdqqIHkyDiSI+PoO99OcmwocUYt+868xdHyN9HIbhAUmMRggoUkEkNjeP+581itMqtXC6xdC7//PQwOyQR3OcmyeEGQ2VnXQp3FAfmj04ZKbRBn977NBYuB7HURnbOAqFi/hc/ndtJ3Zge+3mZkZDTmBMKKbsLn89JUvQmTKXmMI7MUFA7Zayc811397ah1wXTb3exu6MUlS8QUCfS7jOzrCMIy6MHh0XG5qRRRKSP5BBTSfH7/2t/5wu0fw+UxYLf78EpKPF4FgiAjywJqjQeVXiJ9xnmCtbG49fm8eeRNwkL81p4uexeL4ieevgzw4ScgPoYRg4IQJqjmenn+/5Z1liUZ36AbX58T2eOvdaJzuCmLVNPYO4gMKAQBo0JEP/wvRqNCL4ooRQGHT6LN5aHS5hinAy6M3H+biKbdcp5E1T6czlYGB0vR6RJRqSavtXFVTPDAFC46Z+9U7OF/5vkd2PacrmFO1qgJtWLgMN0nD3NT9sP8YvdDfHfV65jN8Wzf8iIDR8pxpKmIDM9hc4eDLubDmetQKJUsy9pHbraWzWVF6KNHb6q+zk5006cB/ukZe1cnltJSBi1D13RILq9Ez5CLmVNHrR5nGxpp6h/gC0smtzZcYE7RVDZs3E1m2uTTPwCxMUZuvTGD5/9WSkeXnZioqy9cKLtdV90WwOdzIknSFaNproUzB/aRv2ABGt3oNbSz6QBB2mhuTZ6LMEGIpN1j40fHt3FdeBg2l4Vdp99BQCRIH8LMjMXjBFhHfydn63YwI+su0mLGW4jeazKSM2t8yLWxu5m2w90j/bl9bhbH5JFivCgLqSgydeoy+ptK6Kru4of33zvp+THqVTywOB1ZlvnF8Xq+Pnt0XkVEQB42Tzw29RZ+Wb6ZuRFpLIzKodnSxtP73+EhZLbXRNHr7CRDjsDha2dwKBy3S8GMrAjChp2mbwI2bqsds++pl1iSPG4nxS99h6hH/g+Arnd+QMjCh9EV3XjJqGW8Xos/nehVWjvr6opRSm5qNj3N7NWfQqcQ2WAZQPDJKDwiDqcWxkyijSIIAmq1TFyUE6+gpcQj0VxsYcX952k8qed8STJN/RH4vAoUChlJlrl+sY2v3HOE/qxBZmaHceQNH6vmPYBh2DLd3d1Nb28vZvPlQ6kDfDgJiI9/kH/GTdtp9dDfYcPtHC6mJkBQkApX7QCKYDWqeCOi2j8nmjT872rQiCIhKiU5XEWuDLM/oiEsbBEeTx92ex1e7+DINInFOUSP20v4PykhUlV3O/HBYSMRIXWdAyyd5n+AnD37Kgv14ejV4SSYcrjXZ+Qnb93Kosi5BJ9V0DJjHZ0aJx1trTg1Qfx0fj6fX9+BcWCI6z+RSf7CGE58178fV083Ddu34vP5EHu7sLzwVxBENGGhBGVPoavPS+uAA6UooBAFFIKAQiGM/XvY1wKgvM9KXLDf4iFJEp/ftIMZYSFXJTwABs4dJLzrMC7XQjSTZFG9mAfvy+WvL5by8Lq8q6oO7B/Y5W3xtsEhuo69j8fRi8t+Al/0NNxuG1pt0NX1fzUIPtTa0byymxuPkBiUwNSw5Ek30asMPF60hh+dbOArBQnkJfstcW29bWw7+TpTk2YSFZrI3zcf4ZXnknn8x2dZOeP+Sa1CkxmLoiMSUKtGrVZuyY1SHOsgafPYeL+pmCRBZl6CbsxvfLKz6/ZJ9NncY5bJsheb1w3oUCqUfCPvJt5tOs365hJuTSzkc4vu56UNv0etuA6NKHPypI+K8licDhGDAcLMV+e4OdjVQv3Z/ciikun3/2j0HJji0MWNT8ZWcfJFCuZ9DfEy+XsuZU6hX8Dsb32W5GD/tTs9K4qzR6G9yYNe78HlVPgrBcsgKMDrVYDgnyL1eGQs3V6CDHYyqjz4lDp6ppbgXn8vEVoXHQoJryySkeClx6YmOCYUfW4ohXlL+N3vv8gc/S28+cL7rHvsbgAiIiI4f/58QHx8RAmIj38AfXgQtrZegibJKXExPq/EUK+ToV4n0iUPB61BRVicEY3u3/91+N9QzKjVY3/Q94bKlFrtnB6yIwAKAQqC9ISqlExqAbqMYej9qkN8Zb7fz2RnSTWLpo5KqgFLGUvn/xhRFBna8igpMXfzvb4m+turORe3gkxPGbUR4Tiiw5hXW0/NplamBk9hr2Bg3+udWH1BtFTaESMHaNh/huRbbkOjn9hyYNi8kYbTwUiyjE9i+H//m5euZivBag+CQkChVOFLmEGPV2KqWceZEhll43E+HTOLqTMLcfZ1oA2bOFoHQPZ6qdv0DMb4TEKmzMTnccFViA+FUmT58iTe3lDN3beOf4hMiDqcvte2jFvsaetEGRNJpD4aTWsLEbd8isGmKEraD/D0sT+wIKaIUJUam0/C5nXzyeyVE3R+eWyDPdSePYtWZxwRBe82HCPXFEVmaPIVt9cpFTwxM4UfFNfzxfwEInVqYs2xxJrvYc+ZjVS1nmVq0mpiwgwsyZ9chvskeVLx4fV6aBooYUvFnwCwe51UueN5r+EQAB5JQimI3Jo0D/dQB83bf0treyNelwPZ3kuUPhzyxpZ0P9lhYXtdD+e7rGOWJ8vVtPUN0jzgFxHNAz66SeVIz3vcmlhIpFZHyp69PLmshG83PUlzXwSyJGIywZyJ3IIuOai+jiYaj76LJn4aU5fejUIx6ozu6W9DMIyvKdPbXolGY0I3QQJC+rpwbvwDsqiG4WiiMY4lkhdFWzMb//wGqogIrF0hSMoYkhMd9PcZEBUavF4Fsk9JTFQjbb2JKAUvOgNIXiVLl3TTe74HTX8xWbEKbs9ZgfVrb+I6H4IQlcJPXs3DpjeyNB9sNlCqVHh9XvKmTCUrWoflbCODAzaCQ67eEhjgw8m//2n3IeFa59S93d0oB9poPiUQrdDj80h4XD68bt/Im9GFkljgj0gINmuJzQqZMLnShx2VKDAjePQH75Fkzg7Z6ffaGZRk6uvriYuLQ62+2Fls4r5K25tIDY3xR2jIMo3dFlbM8M/vl5W9Q2bGPSNvmi5bBlJVC4plC6kcctOXlkp3xZssi50DOOjxNKBXp3Drqm3sq/gYbxans/6MD3O0mZD0SLJuu3z2yLj4WOZMYrU4P1iCPiIOU8ZMuksPEp6cTUGKP8/EUFMF1q4WIqevom7TMyiUKgS1loQld9N1Zj9BsclIbhcaQxAul5u2Q++SuvbjqILNiM3nqT9ziNz5V1MNGVKTQ3j7jcqragugMJoIu3HGZdv4Whvwnj2KadpawjvK+fPCL/Gbii0kBcdzc3QWf63aMfIwHofDx9RJ+q08cYoZy1aMfLdvNBQzJyycJNNlKvNegkoh8v3CZH5ysoEVCWbi9WoSjHr+8KMbGBiAKVOu3IfHJ01aql6pVDE3NIcpsdcTZPL7T0yWAk+hEknMm4ox58GRZVLtcUpPbSRt6mr0KhWyLLO3tos7c2J4fN7YqCyFbGd69CJCDP4omg3H32Ztbgqhnjn89LmX0Ho6yF/2MLWac3zhe3t48JZ0FErwuNz4vF6s/T58Xi8+rxev24fbMjCm/4HW80iaEJKzp40RHgBde18kaPa9Y5LzDfY309q4g/w5n5/weK2ueKJXfhx8w4nVLkQWDW/f86dnmPPpH9D9q19jCYGbW17nfev3aR2Iw+MTsTvEEa3S0ZGEIaKHxEKBP/6ujLyYxVicRrbt+BXmDivq1mDk3W9jjU4jImgqjcEGXtquJtQ8+liqKFPicblJDE+nqvFtwpOnc/rYSbRKJ26HA5fNSow5jCDzeJEV4MNNQHxcAd/QEO6GBnwDA/4Fw78sZXg4UTeuJMQNHpcPrUGFSqNAqRb/yc6BH05UokChyS9GVpiDcbvdtLa24vH4b1qxsbGTGj62157gKwv8t/ttxVUsy/d7OZaXv0dYWDLR0f6Mpe7BfnzJMzFNjaT3xB40Zh2zkhN5zfBJDg0NUHemj/yBlaSd20pE0hmef3E2508cxW0zcsNDE99cr4XMO0dDYntO7yEoefSR63E66Dm1m33rNzN9+UrSbv4sjs5GGre9QFT+XIYay1Fo9FiazyP6XGTd9dWRG3hEQibt1aeuaSyR5skjsE7vOIrb7hz52zvoQtrkr4grACVN7ZizZo34HqxanoYiLhnPuaO0Nnho7ClnMfDl3OvZ1nKG31ds53NTJq7vAfB+e/OEy7ta61CoZRw+FzqFhiGPjSDBQZJp4jDpy6FWKvh2YQr72/p5o64bTkeQmannpz+FZ5+FQ5Poogs4XW5wOyddH5WcQ0/z+RHxMSmiCvmSqIrUtFn0WftoKN+O2+dFAFYbFPSdOAqrHhjTVvDq6a/YjtMQjICSYGxEG/R8ctZqdjgbUTVXc765lq4FqYgD4Xh8oFDCmz/5HQm5OZjCzYgqJQqFAoVKgXPgPDAqLFMLV9DTWkdzdSlZ08eKaG36PNw1++ksteKzW4i47ks0VW8jZ+ajeLxeJFlGEC/4pPivDc/AEKJGy0TxRbIsI9lsKLQ69IYmuvec4uD02cy4/QiKv8yh6bQZo8qO2uBmSmw1W791jOKmarZmJDM0MI9Hj25keqiXXF07elMQ+7LjMXssFKgHCMk9RHL/dHTBY5PIKZQq3F43adOX0extwtHfRLDby9RV96MPCkKWZepPFQfEx0eQgPi4CPvJk8jO4RvW8INCNBhRpySjy5s4s6JODboPVvvsPwq1Wk1Kil9ESJJEW1sbOq0Kzm+H+Jmg93uoNw50kJk0f6Rda5+VNUXZVFZuIjg4hthY/41VlmV6z5wgav4KRFFEH5eGs7SDuBATXwkxsf1MDZ9cnMg7myuQU5MZtA3QfbyaU41KesVmirraiYqMmXiww0iSNLljwCVogkOxt57HEO+f+ggZOkfQ6rvIm3H3SBtdVBIpN/hTo+snycQpSxIl+94jIvbaQmi7hiZ/kLrsDmbfPHEKb4Bjb25j5TK/5eFvWw6w5XAZalEFWi+ZCiUPXvcswrCVYHV8AT11hznb30x+6BUezJdwrrSM1jQjDS0lXHiYzTRfPmLlyLlOpieHojWMzw2hUggsTwjj6FAFB/e1sCzODqxk1qzLiw+P18vp0irU6sn9JRKzl1G844+kTF0+aRvwFxJE9o1bHmYMI6xgrOXq5I7XxrXT9aYSPm0uBq0SSfKg33dydJ3dQ4Q6gf6FmeBrQWvW8c57B5kzNZ27v/dFjq3fQ/7yWWP6O9t0kg3Hdwx/X/5rV2NpZXZIN61H93HGE0OcIZwmq0RLr5qc0GjQgE0UcR4oxSNH03jmEDICf2sUWFd48XkX6FNETupTJggCWDuwvfoEerMLT4qaPZoIfr0oh6aVGipad2BqGELyVhHe7GZzWxJNhkXE9CqZkupmlqWNVJOKpqoinEOV1NhM7AqZiaunkeyiSPq0FtrXlzNjZSbmEB2DrZUIdZUc6D6LU2+got3JqqIFLJ67asyY5GuJNw7woSEgPobRFRYiezwojAEl8Y8iiiLx8fEQHw+SBK3F0NIPKj3VvQ18cY3fkXDLiSpWTU/jfPU2dPpQ4uNHb7Q9JUcx5eWOTL90dNtwuyUkWUYUBCQZohNSeezTF96qHyQbiH5vK912FYquOuTwqJGH6kRIkvey6y8mduHt1G74E2nD4kMseQEx/dp9Ik7ufIOsGfMJCr+2B/uceXH8/rlTfO4T08etu5J8CtaNPoQXzs7kWHMV9xRMHqI4PzKLvR3nrkl8NLYPUqUS+FTaosvWtbmYY1Vd6IPU7G3oweuWmJcYSph5dGpvU3Ud3XYHculepiuTqTqdzIZNu9m8MRynKxsYfXBKksTxk+dobWlDqVKSmZHMYGf1hPttO1yGx2LHYDPj6OxAZTIhqjXsrzjOrPRpqBSgENXD154WV3krmp53AcFvBfH5EJQqZLsF9ZoHEUT/N6DXaPnLy29xy/Wr6OgZoKS8DrHTTeaMcEStFhElBCVwfHc9UlsfKsHOkFqkz6HDHGZgVU42qkKR1/78MimnIwiLHZ/AMC8liOikDEzhyePWWfurCHIoKIhNx9U6gDfaxeLMiZMg7mvpYzFDXJc0VmoUHzs/YfuTf/o9OmMQHlMk1n1niUzRk6428FLz79mw/g6wdNAiy5gzltJb7qVbZUI7oEStbCN3rpWW02YWF5rQGcORW6DpjIVE8wCzNB76Oxt4d4+Gb3z6Pr5zopZHT7WR4DlNqE6LJimIxKAcdiojoOYs51uVI4nGLqDR63HZbZP6dQX4cBIQH8OIGg182HN8fBQRRUgYFhWuIULjH2ZXRSdFyaF0DNjIHCxGpdSRlDia4t3aUo9oVKIPiaOr20rV+V6iE00sXzKahKIwMYttZ/YQrtVSmDEbYXi+OyQhgiCvBxEv5996ivi512FImNhJQPbJVy0+ADz2YWdC56D/bXj+tU3tnD66m9j0/GsWHgALZ8XxxvrztPfaiRmegpHl4TTj1/DmlxIWxYbKo5dtE6MLpssxMG652yexv7mfhl77uHUnqvt4dNkNVz3l2DnkxOrwMDsrkgJCkWSZgzU9DDT2kxGmp9I+QI3FwlfnTKe4+AiqhfF87bksfvJdDYVzgmhra6H4rIOWhsYRK8CUnHTmFPktTh6Xi8pd52jYcnzcvgUBktbOQpIK6a8rwdtUTkOdRNKSNPaUHcTllWgfGGL1tNmkhEXgzZmDeurqcf14yw/j2fcuojkMFEpibW3cMW8e7+4/jcvj5eEb59F9ohrZN2o5mbUgidbT9bhELalLCrFaOzn05ht4eqIp7fcx0D/IlNRknAN2olL814nP6+XUwX0M9VchiJ1kFY4vM9A/0MzZksPIEUUQCwaNAoVz4u/it8UNTDEbeaxwrPCQJYmwmk2A37+lshKe+PYQX7t7C/aObnK/8hDaoCAa1r9De08PIfaZiLlqwgafxXXvQ9iqOlGmTEOq78elVBMf+irNwWre8K7iF6ZYel1e3BYrlgEzroRstOYcqk91YbKKhIc20fbKZ5gvLGYgZBqKvj5sXgXdspeIebP4wow8fv36FnLXrKasqoGpWckj445Oz6Kloozk/PHCPMCHl2sSH08//TRPP/00DcNFB3Jzc/ne977H2rX+pDSyLPPEE0/wzDPP0N/fz+zZs/nDH/5Abu7ERZ0C/JehCeLBxavwSTLbT9eTqKtAlvSkpI+6+/lcToaaqjFNW8LhI01oTRoWzEsc91CLCItlTVgs3ZZe/n5iC2uyZxEWEknEjMKRNmFTF9C47XmcNivm7KJxw5F83knL3l/KYHcXeuNwdIo2GG59Bg7+GpZ9+6oPPzN/Fqc3PUtHXSmJU2YQHnflJFoX89sfL+HXD36B6Ixs0qb5f1OyAIbQ4CtsOZZoYyh1fV2khkVOuF6jUOHxqHnnfCcmrYpzXUO4fRJapUhRrInkCfLhaFSKa/J12lfRyZ2Fo7lOREFgUYY/amz7ntPomlv51Oq5CIJAWGoa9ZUD/HLdWSJmJROTH0xNrczAgItbbprY+iT7JEKzEohfPvkDSRQVmNP910XjUD0pUSmkRPkFrizLFNecZNPJg9wRN3EIsiJ7DoQPgc+DLHlxdytRec2su3naaCOPjP1gBx7DwMgia0MHzlQXJSXvUlFxjkfu/BxnDq5nztLp/pT5w/vf9NsXmHnrUsqOHqFo+UpM5uW0t5/h5MlnSE1diygqqDp/EKVCiVKpZc68ezha56+GrRJFvBMkAK0ZsKMQBVamjPeRqKw6QHjMWFdiu2WAnJUrGZo3j73bNjOlYBrhRbNo37KZ7rZXScqJIrbkFCVWNQ+G3UBZ3XnsadNZIe9Eq2ynL2QelUIOSq+D+NnXo1SIDCae5fjf2xkKz6FobRBCdS1RESK+sl/zsTwr23dZqR5Mozh+Kwm6GL6clYZRqaBg5VpEWaZv0DZmjEqVCsnnnfR7DvDh5JrER3x8PE8++STp6f756hdffJGbb76ZU6dOkZuby89//nN+9atf8cILL5CZmcmPf/xjVq5cSVVVFUFB/8QcAgE+0ihEgbUzUpHlFAYHT9HdvR2TaQZqdTidxw7Qps2ivryLmUXxqK9QRM9sDEGvVBIWMvGDNGn1w9S88xvMWTNH/DvqmstBEIk2xSJeZeSRx9pPSPxFERvBsTBBoqzLodcbmXXLYwhA+YldNJUfZ+qi21Frrq6ejiAKrPrqVzjw7m761RKr145PoDURwX0Cx3eNVqINcojs7tlP6rI7Jt1GHFSSnWhgyO3lkfx4DOpRkbbxbN9V7fdymDRKhEnKBKxaOg2YxuF39iAARkMQeUuSURaX0f/qa9ScikVyOolKTAIm9q1xuj1UdnlpLW4ZWXZp3k3Z7kVhUOGTZXoqejhhUlNU6E8eJwgCRRkzSY9Jp/v1H+C2SKgWXD/GUiaIIsrI0SRqot2J5Br7xBd1SjQ5ZoxRo2GtwSTSeuodYqfdwvTptyAIAnHJufR312KOSh/Zf2RKKsW7TnLDw6NVjWNiCggOTec3275HozeCLy16hOzhlP5DThc+yb9/lShQ12ej3+khVKtCkiTePVxOj92OOVTJRNmCXLY+3ME+rl9Qy6BVTUZCPx6bzLnaYwiiAnOSEpfTxem6OsolC4XW0xw0TkUsTEasKCX7S98maEMx1l4nrfYCTqe4mC29zB9V86m1DNDwpc+gMYUQXJTLSrGdKaEiKfnxlARJHNy1l27lUnrbtNyVH0ZiURzLLFl099fw128/xKLP/BBLVxPbj5zk/749scVRluX/Cmf//xSuSXzceOPYLHk/+clPePrppzl69Cg5OTn8+te/5tvf/ja33eY3C7744otERUXx6quv8uijj07UZYD/YgRBwGSagSzLWCwnKd5bguSNJrcwmlDD1U2BbS/dx8rc+ZdtY85bSOOW54hbcg9nKg5icTkJMoawZ9vf0dZbyZq7EEPIxNU9L+Ab7EIbdNH8ucYIHWeg+zxEXD6c92KUKr+fQt68tXjcLko2/AFDeAI5i+4Yeeu9HLnTksmd9nGe+tUzBIWEMG/u5UNqAVIjDCQtvzhvegpV5aUc3bObtLwcIsLH5ycxqRXkhF+D/9M13vNdnvFOnJcy95YlyLKMr3+Q/tc2EfrJuzBrbhpZP1B6lorf/pr4m27GkJg4xorlQMaXb2R27uSFx7zD+XYUAninx/Kb4kYutY+FGkNoTl2AYkoR7q1/Q1m4AkXUxH0GxUXRuvs0YRflrBFEYVzSt8GOKnSmhDFZXpOzizhz5O8j4gMgNCOBMncbrx9/HZfkQiOO/ibuWPAFige7aB20cbi1i1C9DqXTQUqk36IRY9Qw6PHy/vEm4iWRsw31rClKIXvBVIpb2thdWcOy7HR6h4Yor6xgwOkgTgfbG4ooWJQ2HFWUwKFDUDizAID6+mP099eiMfsozLuesiXTsfbXEB3VSU9LPVuf/wkIday893m6tlUS05hLlbwKryGN3OsykHY34vGFINnNaIQE6jbtonabBY3ByX23LKf43d9yU/t+MDViTPohU7RhTCGO6Eg1Z5sdNLa0sKTsDGV7Sii8bh6yLGMfdNBZ10LzuWYMIbFEpYyttxTgw8sH9vnw+Xy8+eab2Gw25s6dS319PR0dHaxaNeqJrNFoWLx4MYcPH55UfLhcLlyu0XTQg4ODE7YL8J+LIAiEhMxk2nKJ7T0W/h975x0eR3n17Xtme9Gq994lW5Yt995xAQOm9xYghCRAwpsG+ZI3vCGUkJAEQkgChA6m2MYGG/feuyVZlq3eu7RaafvOzPfH2pJlyY2SQva+Ll+2Zp555plZeefMKb/jvoC34zRFVUdIC4/DZDi/Vy00czT60Ggad31CR+dB5t34RwCS4rMoKdlD0eZ1SF4vokpNeEISWRMmD5pDcvUinl09c9N7sOcl+ORBGHMPjO5vJre//CC9zoGCU0N2VMkeg9VhpeTj3xDis5OuUWGPvEDsWpaZYtFRsXUfO99eRmh3E9c++3+ExMUOUtttqKygoq4KaZ+FtPH9wljZw0Yg58iUHD1EZXEJuaNHY7GEnP+85+FS6g28koxwER4nQRQQEPC53egyUxF1A6tXQkbkE5Q7jIY1q2nduhVZ8qH4fMTPX4gvKOicOh+nUZ9h7GlUflUeWVYGG4ECqCJjEBfehWfVWyhj5qGKiEDQDFyP1mhA1KrxdDvQBvvzcmRJGhTa6207QXR2f7ioZP96ULkJCU8BwOF2sn77LkRUXDvvCkIsg8Nqy+qK8UlO5mSkoCgKfy/ZQGVTM6Iym6qmevLjQ5gTHkRNrY2pM5OZPTOV0h2FbH1jJRHhLdTKBt7Z+TmRIyaSNyyPg68/yYjv/IpP3tnPmGn+c5xdVZSaOoHUM2zYDcUNxIWkMlKl5ppntlJXc5TXNm3FcaicSqmX3viDyLowKjrW0rSngSn33YbU68JaegKnIZqEEaPR7mhCjNNhMoaQl3s3KdN+DXrDAA9TVsEkpPJ2Mn/0IVqfF/fBd2iMcLPi1Y+YdMPtRKfGM/Wmq6grtVJaCs88A2+8cd6PPsC/AZdsfBQVFTFp0iRcLhdms5nly5czbNgwdu3aBUB09MDs6ujoaGpqas4539NPP80TTzxxqcsI8A3EqBJZHB3KPmsvxb1OZoUFoTqHG7XL1kZbbzezR4y6qLkNEfEkzb2D2n3Wvm2iIiDLMhOv8ZfLKorC1rdfG9L4GBJRhMkP+f/U7YP1vwStGTn/Zg52dPCdCefWyhjEhKs5cmgl6aOvuvBYoLflT4y/z99G3lp6nK7tW2mqqiRqylRKqg9xPMLLt+b/kJKiQi779k2UbjtKyRa/tsixJidbu9xMTogk0hwGSigrlh6lN6u/9FhRzu3KkJxfLr7e0GgjLfriw7CCWsRTUYXsHjOo8aNKrSZpUf89UxSFyiXv0eRwoJo685LWpVGr6HR7iTD0V9Gc2PQ2QTHD/OsQBLSzrkEqL6R50+u0R0xGpQsZMEentQOxqYFofRKKomDv6Sa4Nw6XphdtiBHJ60VyCrQ27kFQ6RBVauzOk0jSFLo6JcpLd1HZVc7sOdNITxi6y19r0xHqTiwlT3cfd/yfxNz/t5l5wemYNCqisvxv/kcq2tGuOcTikbFoNH7jJ2dqPiZXMF5XBZPjQnH1bMG+fwe2kyUMs9bisx0nMvwAhw5N5rrrBA4cOP/9+mHOVP7v6GZCe8JwdyzHGBTKj298gE+21bFobAQ7PyplgqeCl9zx3P2bBWws3olVdhCfOpbRsoYQRURzXRbqTAv1SwqJmZSGcEbFypatv0Tb1YkS9RN0Zg3Gj16loruRRRMX0/jJM2QWjGTUZZMpLYXv3ggq2cy3HoAdOy7pYw/wL+KSjY/s7GyOHDmC1Wpl6dKl3HXXXWzdurVv/9kxtwvF4R577DEefbRfzMlms5GYeOnVAAG+OYwPMeOQZDZ02DCpRCaHmAd05VVkmS0nD7J49CU83IcgJDgcj9zfi0MQBLweD1VHDhGblYPe2C/sJYoX0BNIHO//093Irs1/4/pp3/4CK7o4/4G7cA+a5P78k5CcXMzJKZz43W8RoqPQ1sG0nDm8+NmvCdVF8MG+D5B1MuIpN786U42xtoeXyo6QHhzBW/fdAVy8EJjKMDhJ91KiLmEWPSV1nQyPu7jGhZroSLwTprLihQ8JCVYz/d6bzq0S7HIQF6MDTRJW34VDO2ciKAwwPADcopHsYf1JzILJgnrkVMIz81EOriRu2swB4z1uF/u2bKa+oRpRFFDptah7O6jdWUyYV4PeYMaUkoLsc6NCxOvx0dagMPfa4WhPeXbSW+LYc2IPafEpfd+d27c/haAPQlAE9IZQHpn7a95/6wM6G9K4RuOlt62EyFH+xH9ZkojfsIngm2biaO7kyEu7CMuMQzSoiRwTTet+0KhbIWsyIfs+RUhMoSc+FslazfybVTz+GIyf0kp8Sisup5ptJzcPcbf8v6tSZwTpmvHENPsQOkS0Rg95GaEUF1aTmZZKweU3851nf8CmNY+RlXkVm99eidW4mPiMZILvHo67phvn9mYMkWE8sbqUaQctTLksjfrNtTQd8NCdHMX9Vyac+rxjsXQ0svnoJibP/jbxq1YC4PPBvn0g+bSotD4CRZz/GVzyp6TVavsSTseOHcv+/fv505/+xE9/+lMAmpubiY3td0+3trYO8oaciU6nQxcocQ1wFkaVyPyIYGw+iY2nOvhmGPWkGXVsLN7G7Oxxl1Qm28cZT0mNRod0qgyyetVeBEEg1pxG694THN/1EQl9/TsUmk9WEFxXhiGscNCUClBTG8Q/3s/m2Z8fYqs6mawv0KTtYh/g7gObsXzrsQHb1AYDw3/xvwBUHt/O8PSxDE8fO+TxXtnLVaOhpKWRso76IcdcCpJPumixNoAt5W1cXnDxsfl9++upbnBww49up7OhhcINuyiY36/maf3jj1GFRfqlQX1e9DOuhiPbMMSEXMplDMm5zEGt0YKg1lG36VXChs/FFJ3i367TM3X+wJb0+zasRQiB4VdcDgKDQmNV5R19hgdASnQKBq2B5XuWE2wMZs7IOYg6M1PGPYTPBzffDFYrpKReQWSiCbn1SRQhj863VyHZ/F2aw269EnWYBR8CoTlOki9LxdPjpu1IG509Hprc4WjlFkrVGrLrXiIu/S6s9fW02g/z/d/8BVCYmf39Uysa2MfmTKzt6+kN7yZ05si+bQVAa3kDlTsPUmbxUR0xhiTVBOxLOxg993+IzYnm6KpqTAdbiB4TjS45mMXPb0GRFXJHhPHeR38mVBVHer6az8bfPSAMlhAeBz4X+2pP8Js/5HDwIR+yoqanByIiRGprAlUv/yl8aRNRURTcbjepqanExMSwfv16Cgr8MWuPx8PWrVt59tlnv/RCA/x3YlGruCzC/4Zc7nCxrr6WeHs9wUEzv9B8wjke8YIokLywX+Ss6WgQsSMX9f0sJRQSagwmJXpwlYCiKKiPQ9AGhfa0YKoqWgj7ArK3F+P3kDsaUUWeu4EdQI+357z7Nac6uK4r38ePp91wscuj1ebiREsPkqRQd7yThJxQBEGg2+HDeJFNEZtaewm36C6YjwH++7prXz2ST+bGxX6tlrD4aEq39Gt3tP/0drRZw/GUlxL+f//o264LsuDrab3oa/sixE7y37v6Ta9hir530H57Zw873n6PyPg4Jl1++Tkrq2RpsIcmIiiChbnzObBqKwd6t6PuDMG5fjUrayaTHuzgmX/oeWVJKLu2utF1mOhuewNt8i8ImjnQ4FQkX19+jTZIR/y0BKK9EpWH2zi534hUmsTesCwKVE0Iegu7OxV+tPC7F101cuWkuRRWlfKPT17nnqvu7pNrT64sYeSti+moqmRkTC6RyQmctIUybEQcnU12ep0+nK39ejG/vnwYL2/eRl1jJZPmziM/PoYo0x1EOlz8uqKRX6THIQgCHp+TOvdJXlpqpU2VyxWzd3O8Zhrl5TBvHoSafaxcrQb+8/pn/bdxScbH448/zsKFC0lMTKSnp4clS5awZcsW1qxZgyAI/OAHP+Cpp54iMzOTzMxMnnrqKYxGI7feeuvXtf4A/0VkGPVkGJMgaBaUrIScRf68iy9AVVMdhSVlaMsjqOjegzF+YFKfcpYpoBI0eCUPbdYe2qy9KAjEhobw7Xv1WK0Cubkgo/BmeTMvTMy4qIfr2ai855ZQP437yE60aed+EwUwKJoLhjtXlOxnZtqoC55vQ0kLapWAAkQF6ZiYFo4mMxKX3UvlkTYs4QZsWrAYL671+966Lq4ePXS1iOT2sWpDJYpKQKsSsXt8TBsVR/QZOhuCIPQ5WWSXE/3o8ZhufAjOeoDLohpxCFn08+Hy+Abdt4t5BMuOrr5/+9xe6vaUIbl9CBqROd+7F1tVE4feWcaEb98y5PExiYmUHDrAsNF+w2HliQqKqztJtau56vLpmELNwDSklibKV1gZM8bA5yv20L6nDKl1IZrxt5Mc8eiQcwuyjMcJzQdbaSjtoNengFbFsKmxjLgymaKsICKSRtEhuShp2EG11oAsy4Oa1J1JRdMJWlpqSIhJo7B4Fz6vm1GJWax9cyk6vUKCTSTpujkY46OJGZGHLMts+b8VzPzF1YgqkYjEIExOL+EF/eXxk7MieLvUjK+rDXVWMlEmv/c8zajn5pgwXqlv49uJUSz9fBN1hpUcrbofFDXRcUHUtHQiCgasHe2YtHYkOREIqJ3+u3NJxkdLSwt33HEHTU1NBAcHk5+fz5o1a7jsMn/m9k9+8hOcTiff/e53+0TG1q1bF9D4CPDVEhwPhhA4tgzSZ/f1jbkYFEXh08+3EhUbylWzZyHPUChZXk7yyIFdVwVh4JevWqWiuaObDYe7GJMRi6Ao/PJ3jWRlpfU1O/tovZcZ4WaM2i/mUJQ1hvOvXZbx1VdimHN+b0XujMXsW/UaExbdN+R+j89HeWctVw8bLLx2NipRYGb2YA0VvUlDekEUtg4n9cVt1CfoyPFJBKnP/dAqrekiI8o8pFHU1tTD1oONXD43HaP+/PdPFxzEjndWYGo5gKEglxxBgLOEz9yI2FwuFFm+6PCcQaf+QjoRis9F9SdPobZE02PNJ/3yfLT6/lByWGYiynqZtpJKIocN7u6bNWIk29euBkCSZSpsvTw+fxydNhcbytrQqLqZOywGbXQs2TPhyBGYPisbmzYRV1Ac6vOURIuSD12zD/MoA6NGZNF+rIOWoiYq1xdy0O3hwe/MQhAEfrTij/xoxh1sO7kNSZFQMfTn2N7WyIH96xk7ahbVdaXMmnotJr3//Kvs7yFU1ZH5wE8HrkEUyRobRk9dK8Epp7x2GhV45AFjXrpyHp8dbaDNetb9MRtY0erfqBozkd5iC5e7S9jmmERVRTfJyRr27DOw90AiOTkgCE5k2YsoXpxBHOBfwyV9S7722mvn3S8IAr/61a/41a9+9WXWFCDAhdGaIO86KN8I5iiIzb+44wRQmQQmjMqn/WApvW095F41mqo3ionOPKX1oShg6n9g+XweWhp2UNmTywMLJ/bFoJ9vaWLWKdVtf1mijlan9wtf0oXCLlJXJ77WFrwnD6PJOnc5bmhMMgZTMI3lR4nLGDlo/ysH1nDPRSTrtvW4CRui4duZWMINzJiRhKQobO7sIVGvJds0tGhaaXsvi8cMTiYvOtJMc7eL667IuqiH/5hFp5voXc3hDUvosXYQFBI+YEyCOYQVLomo2sMkpowZPMlXSPyCHyL73LR++iKpmWkDDI/TTPzubRQuWUVEbuqAa3znr38hNDSEvAL/Gt8+VsbYcH+YMcyiJ0ivptbqpKqjl+zYYBYvhvffV/hsVSyTJxnobK7l04/2ISoyogDxqfHkjc7uyysRkAnLMWOOD0L2+nDUVxKRZSZu4lTq1u/jnc92IKhEVF0jUGNGq9IincNjtPfQelqsTVy/6EFUoor0pIHK1Y1F+7hywdAGb/wV06l8cyXBd18NQPTYaBo21ZG8IKVvjFolsnh0ImsLGwcdn27Sc9TmIAiRzQ4jITNjse3vZMPGPNQmNWq1X0IuJ8dDqKWFhqoyEtMvvfdSgH8egbTgAP+5CAJkzoW2E3D8U0iZCobzi4WdfsI37S+j9JNDTH38WtQ6FeFT4nFICjEjI5EkD12bdtHTVElNxyF6eqqZPPrbzNINDM1Exbl4e3kL7rBmtqwOo7LZRGhTEw5PPEbt4If2l9UgUIdHEPqj3+Pc8CHug5sx3fgwgmro/8L5s25gz8cvDTI+ars6MGi0hBkv7I08XNvF3NxzJ4ufiUoQmBtuoajHwfr2bmaFWQboaOw50crYlIEeKlmW2bixipj4IC6bkXJR5zmbuLRcTi79P8bc+6cB2w0aNZPSR+JuLv5C8/Zz4Uwctd4EmFBp06iudyL3Hhk0xi15qTNGUbhmLXnZWYxK83tAbnvgQTatWI4+PILf7TuKVoCoGL9Q2LHGbkRJ5s5JKX3GhFoNTzzdQfGxVm6+Zhh+pdL+PKSyI6W8/McPyciYxPvLkvnjD7vosDlxFIn0VLYRPzsffZDfU3H9ZePxeLx89sF7/Oa2W1FrNOT0tLNqyy+4bu5zfUaSx+fm07VvkJGRz1Wj7zznfZgSXEBMet6Q+wRBQFD3eyLUWhUI4O31oDH7/684u7rprmumqtnBR55mfMoZwU8F/s+rIkjW85xQzv6Gw1zx9xQ8+9ah9+qJtLhIHRWPSqUnIXMy1qaL94YG+NcQMD4C/OcTmQ0RWVC9HfQhF/SCCAi07S8hdngsmlNv6WF5ETTvaaJqQyXxU+LxdEXTtL+atLmzMZ4jrPP7/5fKzTfDa09Gk5MDaTHw5LwxfHKyilvzsi/5Mi7K4S8IGC67CV9LA7ZXn8Qw+TK0I86h8DrEhEuKNvGjqdcN3nEWzd0uws3ai1JdPZMRQUbSjTKbOm0IwCiLkTCViha7m4lnhG/sPR4+31TBzKnJRIQbzz3hBYhOG0lrzdQh9+k0ehw+z5D7LhZF8vk7M18odKMoyGFecmZdPuTulUeLmTE1k2Cdjk2HD/Pu6tVYzEEkRFqYMm8uv9r4PqaELO7KGUOkwW/k7m9rIKK6BWFE3IC58rIiaKofWoxR0uew72g28XFl7K84ypGuYvQpqRyt2IkcqrBnfyE6tQ5ZkbG5urlz9vfIGzmK5vo6ElLTMOvDiNVEYrPVERycRG19CWWrPyaiXWbkFedWqd5RtI5tti00rvULRp42G+psBuJVfm+XtdPFofUVfcfIikzvnz9hzsgUUEBl1BGcFMMNo2MxhRjQqweGTXo/3UaYpQtfQhyL0tPZ3FaILy6FheOmUH30JKNmLOgba23qOOdaA/x7EDA+AnwzEARInQ5lGy5quMcrEHZWKWbMxFjWv7kV57Zu0q+cjM4wOHzwycFPSAhLYGzqWNRq+Pjjs0fo6HC6sLncWPS6AaWRuUM31/1CqKPjsXz7f/Hs30DPu88DYLrqbsSgfkMpIm0Y1cf3kZLrr+LZWF7E2PisQaWeZ+PyShys6eKK/NjzjjsXRpXIvIhgZEWhsMfJ7sYOJqm0OE90IurVNPp8FJ3sYPEV2agvUs32fCjy0OWVQVo1exxu6vd+RoMVdGGxA3u7nDXe3ePjw88bBmwzOESi1/+d2pIqUvInnnWkQGeLjZOeIizhWsZO+u451+iVZIJOKaLOLiiAggI8kszm0s/wHN3I/86+C43exJbGo1i9HoLUakSVhRMhRrJ3l5A5eWCIw+n0cuRIE6NG+T+j0lJ46ilobobDhwW2bcukvdfFw/8byejUON54Y9qgNbV0NvDB9teYFD3dXy4NIKiIsCSz59j7mJoUQoyJzL7/F2z56aN0FBUSPmKgYd/SUM/SnUuYNW4OYVMmMHvEfQN+v/6yazPzJ8/iXCwX2zFNyCMi+PwGqCzL5OmdbGjby/UzfonX66V++27ysmdxogacXYco2df/2Xm9HuDrDbcF+HIEjI8A3zDO7ybv8tlRAwaDiNM+OD9j1m1TefiPj9HQfILbh90+aH+QPghFVnhj5xtMz5xOWtTABEK3z4dXUdCeqhb45BPIyqIvKfVMueoviyAI6MZfhm78ZSgeN71L/ow2PQvdFH8PpvRRM9m79C+k5I5HlmUONh7nJ9NvPO+cHb1udlZ0cMWIL2Z4nIkoCIyyGBll6X+wSHYve3fXkJQV+pUYHv4TDf01ZlKpWDx2AcUHlqPr7GT6/EVDjhsKr9tL3fFK1JpYyopKSbtiAVFZKYPGNe46QUTvKCbPG5xbcyY+WR5k9GlVInGRiVgSR2Mw+XNW5iZOAMDm6eXDiif5n1nPcHzLEY5vOUx8RirNRVZEEYZp9Cz/1Mof/xjLq6/CfffB/v0QGe/BIws0NAlIXj1Ou8jGLR5AOyjsFx0WT4etFU+EC5WspqZmOy11Sxhe8GMitxain3UFxlN9k2Y++zy7n/4/8hISKT64H8nn/79jMgcRkZJEbmoBdOj5+OTH3JjT/zvmlRRcPg969dBhyOXvjKejZyv3XXfuJolOu50tqz9l/LQZtDY6abe1E2GJwGQIJlpvob2xG0u4mozhi3DUr0Nyd+ClBbwu0Fxc08YA/3wCxkeA/ypCNSasHonkmcPZ//4RMgf2SkStVjEmYwx35l3LxIyJQ08CjEsfx7qidby1dytz02YRGWTg0wMVNJjU/O+Mkeg1/v9a5eUw5tQL2Nm9MgbRUc6RQysIj8okMcEv6S3VluEr2YugN6Gdec05DxW0OoLu/B9c+zfS88ZTmG/+AYLe2Bd6ef3QBm7JP/cXvCQrbC9rQ6sSuWpk3DnHfVlUJg03z82gvsvBgerOQXkg56KosYX9jUUMi0zFJTk41lrtz0mQFRK6d2Lb1wzC0Doux0/UMi3+WiqWbSd25giMYSEXPN/2JevInjiCzsZWJiyePWQSqaIodDbbGD3jwi6tNodjyO0qQYNPHmwE155cw8Isf1VT7sxReN0eDq8/jtXuZvpVo9AbtJR9fJL335dYvdGHrUONxyvSUDVQ46K2OAqfD6459asTfJao7G0zHuD9Ta/gUNczP2MWvozv0vGKjW5bEqrkLqSyT0BRcDocdMYlsGnVCi6/4eYBnZjL9n3gX2d4Lic6T/DWsbe4c7g/N+Rb4yfz1Ia1/N+C/v9op42gn/0MenpUrPlgEgbtVm67csaQ92jLmtXMu+Z6VGo1iyIXsWTfEm6fdDsZySFkZ4UzZXISDcufxXr0RUJHPIAmKNFveNTuhvRze10C/GsJGB8BvlkYwsDeAabw8w4zZ6XS27EDt7UHXUh/8qWiKMSERJ/X8ADYVXOSw+0KN42fj8qjoqyxg0cXT2RLvZVXjzaSHx3EvJQIMjLg8GG47jou2Ctj5GV+rYYjB1eQEJ6MZ8dKhLAEdAtux73hw4u6fP24OWhzx9Hz3h/Rj51BRHQKR07uweVzkxgSMWCs0yNR3NiNzelFEGBiWvgXLhO+VBJCjTR0OWmxuYi2DP122uXoZUnRNjySl7TwMK7Nm0BRSxWCoOK7ExadUTVy5ZDHAzQ01KL1VJM9ZSKyJFO/8TCKIpE4dwziebQsIhLDMFgsjMhOOueYo7t24hUrqKyphyHbVynUtknEJozGa3PyUVkLp1vmKKf+WB29zIof7BXweBzEx/eHDTQ6LeMXjWTdR/u56SYRuwPa2pKRZBG9oKPDDQOyM08ZYZIEsiSwfosbc7CXiMRu7r47njfegJdXPUNUUDTxkYk0W1txN7dAVBgvaY8w49b5VNXU8r3bFvetYceOHQwfPnyA4QH0yfYDLM5czJHWI/y98O98O//b1FibyT6jIaPPBw8/DMXFEBQEPT1QXqHmhu/UsW/boyQl30JM8sAScIvZjOpUKbVapUav0VPechKru4mICL+eR9zVb+FZ/QYa0ynlXI0evM6hPpQA/yYIynkbVvzzsdlsBAcH093djWWIbo4BApwXyQeVW/xVMEOwY9+L2D0FTB87lrJ3t6Mx68m9qT8e3treyvpVHzN6WHrfW7QgigiCgCiI2J0+1rpkhsfGckXuuRNbn/h0L7HxRhalJvPw/Ra6uiAnB+z2c1e7KIpCdVsv5QeWMV1rRDv9aoRTVTO+skIURUSTNXQ1wVA4Ny3FU1HGihAL8XnXoFapB/Sn0alV5MVbCNL/6/QQNpe2MiunPxG1vruNT0v3IskyRo2BG0ZMJUh3fv2T87F3/UYmXDZnwLbqwycoLjpEdsFwMkcM/Rkqssye5ZuYdN3Qv0cAbpeDkr17KZhx7rfrf6z/nOnDxqIIKkStDkFQQPELpQmKQkNTA2FNJwgPC8ZndxAxdgpak4Wi4o/IzbkKtbrf43Jo20k2bgvi1bdjiI2R2bVTwSudNqBOG2LKWT/7SU2F3l6IzW2iIDWWN96Aj7e9zvXT7+kb43HZWf/xz5l65f9RdLIeX4uLxqZa4jJCEVUCMTExZGVlDbrGPTV72Fa6jZ/M/0nftnU16zCoDHx6uIdnFy1EkgRuvhlOngSNBjo6wGDwGx8dXT6cdjVVVS/h7gpGbwgjJbc/cXfH2s+ZOn8hzh47VYdO4vZ6cAd5GDtqIuozZOk9x4sQXW2oC055+Ky14OiEuFHn/HwCfLVcyvM74PkI8M1CpYZzJCAC+GQP40aMYG9hIRMWjWbrb9eQc6Nf1bLykx0cXV9NtF4kbKa/K6miyCiy/+89W4+j+Hw8et8tqM/z1gyQZnCBYytbGnP5+ONzv5kD+CSZzw43oNGIpFoMzJ5/x6DGaerMfL/34xKMD8P46ehjdNw57OJzHf7ZyIrC4cZKdtQUARBhDOOe0fPRa74ag0gaom2UpBWYOm0OrT1NbPhkKeNnzMISOjD8I4ji+RsJAps/WEnupPOrzUaG6siIjzznfrU1EndMCFH50Xh6uugqPoSgElF3NFDsfReV2khXZylG8Xoqi1vZuSGTREMjs+NrOKAZh6SALA9tcJxJZye4XCDUmCEVGppqcDgGyvBr9SasDcEc21ONWhSZvHAUh/dpyBmWiSl4aO+UJHmZmDyRuta6AdvnJc/jd/t/h8UwDIfXzecr9WRlwdixsGGDPxwZEwNtbbJfVwcAhZzRt1Nbtp5j+/6OcEroTG1vp2RzNBqdjuyJeah0Q/9uCHiRpTPuQUgSNB6B2JGX1HsowD+HgPER4JuHOQpsTWAZOmnSYtRhdzgxRIcTkRTMnqc/YtLjN5K2eCo+lwdNZATRyQPj+J8sWc/0udOJTL64fAhfRDzDo69lfGzGecd197hZV9rM/OExWIznb7AoqFQoXi/CxTyYm4uhpxnh39jwAAiytLO7sZDvTbjpglU4l4rk9aFRDb5XBpMJye0jK38kGXkj2L5mFWk5uSSmDfysVOrzfz1qtU00FHUSZbaD7D1lqHrRRYxAbbq43xNRp0Jy+Y1lbVAo0RNm+ncclwhNHofGaKZw90oi48MZOy2P95ZUotPCOzuy8Hj9VcCDvR5n/1s4pT6vYNRLVDTX8Omqldxzx+DS2YzsGUyY3+8N0ht0OO3uIY2PFSuew+12cOON/zvktcWYYpidPZanNmyidsdIzEG1eO1aio7n4HLraWhx4vFpUfeV1PqvIylzoDhYk341sfmjhzzHmWhyR+Ne9Q5yZyNi2Kn7H5sPbaUQ9RWWmgX4SggYHwG+ecQVwInPhzQ+JuTfw94jrwIjAMi/by5vPbKWjh99hE4j4XFLZExQceKDbaD45Z/3t3sZMSryog2PxnobXRVOqr0Wulr95X+K4A/inH6blhUQBQgyaJiZGXVBwwNAPXomnv2b0U0+hzqpokDTUbA1+HVPzhF6+ndifPxwHHIbbtmNQfzi4ZXTKIpCr91GkDmYgzu2M3zS+EFjtHoNnh5/PoAoisy4/Eo2frKMiOhYDKb+niAdOg3Pvbwak7OVkYmxqHVqHKqlxCVcRfbIBaRowoifmIEse0HUIIpqEFTYS94heNxPBp13KFSicIbn4gwEgT4DQpR4v7yd8FYv+tk6Nr0YguwTkJSLMdb8czgcMoIgkJlkIi4mhFxDEhrNEOq1Z3kIktPiObK3lNYNbVx92zyKi1chSX5jKT4+FUXx/9vnamfnwZeJDM1mdbMVtaCmrrcLVedn/GrenTxRfIDiIy6e+s7HvPz8b5F8Kq663MzKldDdPXgZvp4umva/jaBSY/e2E8vQ+ilno114G+51b6ObejmCWgvmaJSyjQgB4+PfjoDxEeCbhyCA1gguG+gHxh0NxjB8kofQYAN1LU0kRsdy5+/ncHzpHtLm5hOUNLiPSaLTy5qddZy/mBK6Ohzs2FdPdISRR68bMWi/JMuovsTbvRgcDg7r4B2OTqjf7zeWYkf+x8W4p8VPY13NOhakLPhCvVVOU9tURVNJFcbgINxuF6bwYEz6wQ3GdHo9dpd1wLYZV1zJxpXLmXftDewurKSsuh6dVkdcvBlZMDPlyukc2PQ7Is0T+x6+bjkUXfxggbf2v/8Je9vLHK330ePUsaOkm4jxBUTEx2DWa9Cpxb7rFFUDjQ9Xr4PuLhteKYqKPStwSh7KO1s4prfj6apn0bRIdr51FdYeATi/9L2f03lLYDSI3HSTyPbt4NVq+HT1G4g6AUWRUak0CIKA7BwYTjRbTEy9bAx7thxi65Yt9PSe5Morf4Ao+seVlKynZs3TTHF6SbrqMZat/B33LHyEYL2Rj1dtpbFwD59tv5+Ik9EcLLqKG++/m9DgTjqskZSW+kMvp40PqbSe+tq/+P/tcxAz9g50wdF0Lt0y5JW5PT50WjWSJFG+/QUMpihUahMH1a+xeV854yPmEaNW0aDEcaXXR7Am8Lj7dyLwaQT4ZpIyHU6shtzBYYeJo+5lz9E3KSp1kRgdizbYzMhvndtLYDRo8A31dnoKR6+HzTtqCDJruWJ+xjnDB1/G8DiNEBSC3NmOaNL7FV0FlV9SPuOyL9zh91+NSlQxKXYSG2o3MDdp7hcyQA4d3o1R1jNhzrnLiU9jMBiwdnWdIUoOao2G8TNmsenTT1hT1ExSSip2eonPiEIjiryz6Q+MDgojPm06sqyi5MAyNFHHgKsGzR+SNR7z5Q8SBzhau+ksa+TQ66/w0ahIQhMTETW6vmRmye0hu0kP1hgURUGtURMaHoZWHUx4wiw+Lf+cCSMvZ2yIhZLypQzz9nDV5C28s2oR4TG9uGwanE41PulMQ6Q/B8RiEbBYoK0dvF44dAg6e7uQo4NZNGV+X/dan+xDFERefOUp9q3Y3DeTx+1BpVJR21zDzOunUXhkC58vew6NXo0hxEGwOQ2zdILwa94AID04A8upBOG42ChWHTHhLW5HUKz89Ja/MUF9Lcsrm3lrwxSsbongoCCiTtn7emMSCTMeHHQ/hXM0ubt/0yF6nD3E7CnDlBHL7+6/GYCqih5+HD6ed5vauCV3ErKisL7DxvyI4CHnCfCvIWB8BPhmIooQnAAdFRCePmCXTh+MJLlRZPkcB188e/fWY3N4mDcnDY3m/EmoXwWaMbPxrn4RbVYWZC38jzU4ziZEH8LE2ImsrVnL6KjRRBkHe6CGwul2cnjHDjJyhhMVf3FhMVlRMBgHe0RCIyKZOHUSSa6/4urazPrMB7lt3Bh2NewgNH0xw8JS+8a6S58lctJTg+bw2q1IThdSdzdN+8uxt/cQOzqNWd9/mAlGFStXbePOWy5Dp/GH2docbRSXFjNs9NCNAi1tIRzpLMbT7qGtohFRnsyI0B4kr0JXRxCiAggyIOPX9/D/rVIrSJKCXq/QbfMRGipjMqp46SUor7dRXSv2GR4A6lNCbYk5CYyf7q/ekWWZ+i4r2yuqKLfUkXBkOaHOLEZfeyv1h8pobnmTqIYtiBN/1DdPcv40ijZ/iLq+Env8jXx7ykx0o5s5GtTK7tZWyptP8MCP7+Bku4GD9TaiU5tIChX4pLqC4O5awj85s0eP34hS94Jni18u3eo9ijc8E1EdwnidG2doCmKki/geH+v/cS3jEqKZPPcllhz5DSe9WTQ57cQaTOhFEYckY1R9M/6/fBMIGB8BvrnEjYLS1WAMB0PIgF35GVfwwaY1SNK0AV/C52Kod/Fumxuny8dlswa3Sf86UGQFV1k36pE3Q/KXVyD9dyNIG8SClAUcbTvKsfZjhBvCyYvIG6AjcSY19RW0lNUyftpM1NqLr44RVAKyPHTn1rbKQjLmfYv9x7byvbHjEEWRCFMcx7obONxRzQ2pk9Cp9WhCs/H2VCKqhyOI6r5ciQOvPkl4/CiaN62nrtHB3O/1N2ILAlIzYvBJMqcLNrySt+/BfyY9DhdLln1KWmwYs2bNQpZFbnpe5u3SdnKj7RSkHKfDk8CJiihQAYKAgEL+MAdZ2e3kF1Twu+cnIai8zJ3TQ0tLIpmZ/rkzEpLpsHZxpPQ4o3IG5kLUdNt4a89+RFFEFCDKaOSmMSN5vayDkam34Gs9SdXulzEYk0jNuBx17duEJvdXYIVExBAy+ybca99h2By/0e/t/QnBB99GDLXSETydzJRorn2hk6sUhTvjT/8ex3C0swPD6MGepDOJ8C2krfC3ROf/goTqnSxOKeCPdeW4Om2srchkyuwrad3yczaWSRhG9PBWYzc/TTcxJcTMpk4b8wLej38bAsZHgG822QuheCnkXgVnSDxHRA0jPHQZ+48VMTF/1Bea+uCRJqaMj/+KFnp+fJ0uPA096LPDELVfv4flX8nISH92Taerk811m4k3x5MTNrCk9dCBXRjURsbPunQFS41Gg3wOr5fa24ugD8YQmYHPbUenM5MTlkFOWAZd7h7WNBRRbmvm4WEP0HbwN+zeuAV3VwtBope8aTmoorKo7bZhDlFjcoVSu64GBPBY3cTPSWRiwShWrN1ESEgQCNDtsJIcN9jLs/t4LY1COGZVOK+tPYiuYwSZWVoeu3snG5eaOBaUx/zJDbz412AyUjRoBZETFb2kZRvpsKayY0cqifFg0VZyrDgZQaTP+ADo7XGRkBkz+AakZHDniHGDNi9OHs/W5sNckTyJkMT+ahibfR+KovSFyhSPG8+OzxBj+iuHVGoDxu5crh6Vii8ohvu3HeNbwxOYFjWwA7VHaqCj6LTno9/cF1Q6wob5K3NEtY7g2LlYy96l0esXzdOVN3DFbVcxatkBpMJSqtoimVXTwzrPJrRXLqL7VL6HUSVi9foICeR+/FsQ+BQCfLMRBBh2NZSsgKz5oOtXMy1IGs6esuIvbHxIPhmd4esV6PK1O/G2OlBZtBhHnFsv4ptImD6MOUlzqLXVsrVuKzMSZ+Bw2jmycxeZI/KIjP4y3p+Bvqye+hJs5XtRnFbQBiFoDMiegQqZobogrk4ex8eVO9CotHQLueRflk5rZTsuh0TqFYtIPWN8zef7SJrnzyzxuXxULytDE2FkiiEPySHRbPWgF4PRmf2enbrWLj7bX0aISYe118Wj10wmyKinzdrL/zxuwyQUIRgaSF9UwO53FGqO27jiwSbmjq6guS4U97vhfPvby4nRWXj9/VHMndjB4mt87FjbzJaSyQPE7WZNGM/K1Z8Tv+iKAdeoUw/9+xxpCMEtDaGfI2pQPHa8h3ehuOwgS2gnX+6X9j+FbVMd0QtmUFNWRe+JYm5xtFN0pJlCoxm9Ss11I3IJMZspVyfRq0hkxOaRGNnvTew3SE6tMXIcts+WML5XZpXNg2niOD5a9gK9sofcCpnK8ESSzDVMUrKRqg/zpj2Ph4cnMiXEzOr2bq6IDBnyGgP8cwkYHwG++ag0kHcdlK3za4DE+ePr2TmLWb53aI2CfyVSrwdPQy8ooA7RYRh2fqn4bzpJliRqbDVUV5XRUlNPXt5EOo9bsR6pRlH89uXZYTGnzYMgCuiDtGeqjQPgc0t4VV68Xh9VJY00VLUT5ysm89q7+pKFk2LSKT/6GQURKYPWI8gSu1b+iciEdFLy8kkdKSIIIEuS3wMgCIOSZtV6NRm39oc4fD4J9+pqIidEc+xwGVUn6zhS3YRKNJEQEkZiiIDZ4M8LiQwxc9VsM5tXxnCi+wS2rvGYLQIzFkTw/toObv7xbF5/XSQ9TmLB/B8CMK0djhyBjJHw6l+KB99USSLiUC07Wp8i/5p7sYRGX/BzGCoRWJFEPEt/j2bWvahiEwbvlxWkbg+CRiQ6PgZHiw2DTuE7c+cgiiJVLa28uucAP5o7k9ruEDTKGjCkkDjAzu5P9u5d9g96Vi4h9N7vM8IcgnnbfgpN6aRo8nFILnytBsxiO4dMFRwLn8q0Kg/DqttxZsRi0KlJM+g4aXeRZQo0nPtXEzA+Avx3IAh+z0dXDZSuAo0REidg1Gto62whMmzgl6+1xYG1xUFwlIHQmMHJiV813hY7vi434G++ps8MRRADqoynibXFU725ifj0VLqreogdG43OfDGlpoORJZnNyzo4sKWExMwopl4+kradnQOqlCyGIAStEfmsbrQl7cdJVgehXbIGzZW303D8IxRFpqOlmqKek0RNnw8KRJVZyL1ycBnuadRqFS6bh7CIYBJTYlGJasZlJiOp9IiiwMbDTYzNDENv8l/j4sWwZMlwfvVsGpeN16BWwc03qPnHW8FMnu5hzEj9gNwl/3iYMwfioxJpqiin5J1aFEClEulusGJecBd/eEnPd0wvMummR/D5vFBfD0NIYsiyjM/ZgyzLSI5u1KYQXG01tFY1kDznQVTRgw0PAEEUMI2PoX15OS6LFm+zl4aqetpybUQnh5AaHcUCr4f39x3EbYjE40lCbVQjyx5EUXtqDi0dRX8ifMQjmK/9FoZpC7CveA3tmMmYF8xhplNFoTMD9ZbdVHW6SHUYGTfsFn5923Te/M3zBCleinYFM35WGrlmA6varGQadV+qrDvAlydgfAT47yI02f/H64L6fdyRncm+nQfJTfO3Mj/9jhUcYSAlP4LOJjtVR9uwdrqQZQXxKzQIFFnBU21DdvnQRBsx5Fxch9f/RlIzU9CrQknIDr3w4AsgqkTm3NCf1+BxeIaU3zZaYui0NhIR1v9gVdSheA+9T/5f30Br6TdYQ9rqcK1dwtTZdwCw27ueDmcXZiIGzQvQXWsjPMWCqBJJy0kctD+8qgvfGdXdajV8/DG8cKCVh8f2Fwkv/1hgWWEh95wlpnZ6PICiWDjwmz+Re9svEASBxp3HyFg8mfIaLYIAOksoe5f/FXdXO5kmf++WvQ0tNBYdQ2fQAQIeWSZX30bVh4+hGER0QXHoQmIJD74M5POLw+nTQ2gq7SR5ViIJYhK9H3VRtKkCe0ESrl4vWWNi8MqwraEdt3YGDUc+Ia56JBHZmciijJy4gIqd92JzdpMYfSdNnkaCFtxE07JXWLbVRsN376PaXUqERsv0CDu9Nhu1XSXkHDFw5U03sPeVt9AelXCMi8JoNjMpxMxuq53JoebzrjvA10vA+Ajw34lGD6nTCQXmD13lCEBYrImwWBOLk81sLWvD45OZlB6ORa85oyfFpaH4ZNwVVhRJQZdiQTT+6xq7/aeg0ojIvi9fGj0UiuRFUA3+KtSZI3DbO+EM4+NAazGqLoEJpoFGRWhkItqQUI6se49R825l0oLL2LdtC5FxsciSgiT5kGUZRZJRPFC8tI6cOYONjtNoBYWPN+wjxKxFp1HT0N6NxaijyurjT642InUC7b2tCPEhFLl6Cd3wFmoB/DUvCmK7RKIqDp3BH15odfmoWbeOhDnzefgPw7FaITfXL/D1wgd39uWDrFnxOb8sayDL52ZEiIWRE8eesaqptB97ldCFN6AK9leNuI5X07ulEEGnBUVB6rITds88xLNKWiPLfooYdzUkjEOWZJLywlFrRPRmDYgQWXGCGZ3dFOvMjM5awFGvFcPyJXgP7mLTDUlEqVIpL91MxuEeGhwS8/TpXP3wczz6fZkdT36fLtceXDPj8DlyCT/oQNPtwbX8STT3v4LX68XYe5x1e2NZPGc6YRo1Np+ErCiIAe/Hv4yA8REgwEUQGWJgVogBRVHYVdGByyvhli7O+FAUBV+7E1+HC/CXeurSQxDUAc2BS2FIGfKvAMXnQRiij0uQOYz2uoEN02bosqnUtSEOUZ49ftF9bHr76f7jQ0MoLy1BEEVEUUQQBQRRpKiwjlnzJmEM78876Kkuw1F2FEQRAYGE8mLGXHE3hvAo3G43apUKS5CJG88436oNb3FF1kIY3GgWWZbZs3kjI+bMAMDnEen+8ff5IN9JVtZinnoKXnnFH2U5k2a3h1+kx+Kw9VDe3jxoXl9LK8IZEvT63BT0uSl9P3d9st0vO3LG7fG4fDxnuJJHtdEEByeQNyOMlqoGksb5jyt563WC4hOYn59HXncvXT0K146ZDsOnw/1A3RH+cXwpM/MXc6yxGH1TFxvdpcg/v4POyAzih19O6gEXFdmPsqK4BJPZyXXBRl7f8REz/7yV6mA1QcQSWitQ0dxDekwQU0PN7OjqZXpYfwJ6gH8uAeMjQIBLQBAEpmRE4JVkGnsknMc7QBRQB+v8HgyVgGz3Itk8KFL/m7omwoA+OzQQZ/4SGIK09HS6CAr7apMFe8qOY4xPGrQ9xBhMvbO/8Yizp4eqvQeYffst55zLHNQfOssdMWrIMfW2HtwhPiJjTKAotG35BMEYRvRl1/eNEdS9WEKC0Bp0mAzn6Ptzjt+lXls3J8qKqWqvJK+pE1Okhe49B4n65aN0fJbD2FFw/fV+w6O7G0aNctPV1kPxgcMkaFVoRBGdXofX7R4wr2y3owoNRTxfwz3Zh+yTUGn6DWutXs09idMIHu6vBZJ8PZzcs4muqiNoKirRxsSimTCCx/c8QV5EHkldI3G53Oj1/uu+KnEUVyX67+Vr1lIMU2ZScmQn+3rsxLemcMKnxjzufuK3rmSuqpK4Ed9iz7YNTJh4OebqYGYPn0gdVvQuHwdK2kiPCcKsVuGSZXyygjqQW/UvIWB8BAjwBdCoRJLH+xU1FUVB6nYj9XpAUhBNGnTJFgRNwLPxVRKdYqHsQAumYO0gt/4XRXa7sLX2kFYwWPNCFMW+HCBZltny4svM+fGj553P6bTR2lRBcEg0OsPgnAJFUejq7CVhYjRum42uzR8SNPEqTNH9Wh9uWzuu4hWUWo2IGi2iKKLWaEjIzCY2qT/fo8fTM+QaNh5Zy5Qxsxk1ciLVK1/HlZRF8sJsfHX7SY+O5PVnJZIM5URGq9jeMYG2olq2PPoSOY8+QnbeMAC0Wi12u4P33v6A5LAQ//2orCAkKYlzFXy7TpzAsX8bilfA195DxL3zEE8ZEFqLjtJ3DtAuNCMhMOfe+9GrBWpXfUbaTX5Z9BuybqCyu5KU1GTqK6rIGJ4z6BzuiGGsL9/Eo6poTpgTyBmVS0pBKt6D25ELs0kxGVi3v5DhkRZMigN5joGMccNJaDpCTWEpTUfbedXoQG9S4fBJ1PiiSI+5ODXdAF8tgqJ8wcD114TNZiM4OJju7m4sFsuFDwgQIMB/DZJXpvxQK+kFkai/ArG1so/XEN6wAcEjoUlKRTQYEMOi0Y6YghgczqG9y+ht1NBWV49OpWLR97593vnqSg9gb2+ms+o4osHA+Gu/O6BaZs2O7YweNgyz007Pkc1ELbwdQRx4HYos03ViJ92lW0lc8DBoDHjdHjYt/RsJKWEUbvyc+JwcOhUX19/67KA11DRUUl1fzowJ87DVluD12ghPnwiAzwcFBSD53EQnVtBmiyA+Rsfny4IGrNPpk9naZcPmk7kxtt+bs3vdRnRAREI8kizh9fqIbG1BlHw4Dx4k5Oab0Wdm0v35XkyT81AH94dojr++jsxbZqA+ZZAoPh+V779L+h13+X9WFEo6S8gNzeWzrbu4atZUunvtlHd2MSapP+/mj0c3U1Z7kBu6IlEHh5I+ahRREVE0/OkdmstKibjmWip7lrM9xk66t4vWnQ7uueMJwjLyqd/4dxLnfLuvLcHx48fJzQ10vP2quJTnd8DzESBAgP8YVBqRzHHR1BS1YwzWEZ3yxV5QFFmmYtkOtEERhD3yO/82RUGx9yC11ePcvAzFaUdxi4yavBDb/j2oH/nBBedNzPEnaB5UnMQk5LDvk78y8drvAuCVJBo7e8jtVXNw+1ZiYw30HNlBxugZA+YQRJGw3Gl4nT34XL3oDRbUag1JBXHk5d3EyOl3A7Dt5MtDriE5Po3S6mKcHieWpGE0HPoEyeMEdwd7Dq7hppsyKCsL4/XX83j9dZG3PxlYZryjq4eVrVa6fRK/yx6YFDtp3hxOlFdhR0Yj6ulsbEayO0kdlo0uOxtd2ilxMJUAp8KOiixz9JmlBOXE9Rke/jEq3FVVOFtbMURFIQgCw8OHA5AQEcbHn63FsXs3tfMWDDA+fjByFsXJ2Txz+H3yjQK9jUcwvbWbarub21/7HYIoYimL5NHygzyZGkPNye2oI+IQRJHgUYto3b+MqAn+EFd8fDx1dXUkJp47+TfA10PA+AgQIMB/FKIokDoykp5OF1VH21BrVcRlhqC6yATe1j1FtJQ2kzJvLEFx/aW7giAgmC2I5mFoUv3hh+w9e+l5/z3ifvW/iNqL1xUZM+0GAGxNNZQXbSc0fTR/3LaJjrJQjIY2br7tDvateJnIkdPPOYeg0iD7/KqiTmc3HZ1HkKRrUZ9qE2B1uPnsxF4UWYXd66LX48AjefD5ZMzdbRi0/hLYmLyFNJeswdS0gmwkPJPVHD48nRkTOkiLbcHVauCaqx38+onDdHbU8m74XH6dm0qUbujrzc7w5264enupXr6C5FtuQh83MGylS4mhZ+MhOBUei4y24HC1Drw+QSDle9+n8rVXyXzoERr378fd0AgomIAJIaHE/fIx3j9ZNWgNeSFxTIrJZ3ZUAi9teZLpyU1MFIcjnDKiQpOTeeCdl9nc4iMlcgyVez5FZQzG2d1OhMNKxFgfokqNxWKhoaGBkhKZ3/5WHKACG+DrJWB8BAgQ4D+SoDA9QWF6vB6J+tIu1FqR+Kxz64D01jRRvamY8OHJjLj7sgvOLzkcdPztb4TccP0lGR4ABz74jJz508mdvIid7/2elS3d/PKyBRxN7GLscH+OgSh7MBr0NJ7YT0tdBSqVmsjU4cSm+MMAgqhCkX00W5t5b9v/IzEhlWXFv+X6/McRRRGVN5HM+GQEQSJIbyBYa8Kg1bF+cxWTp/X3HFJpdbh89TiSEpBkJ/G6Fn724E9IjZhLxMj5HDho5Q8vWMgYEUdn8wnurdqD3K2DqJRz30uXF5PJhCYtjdi4wfky+qwk9Fn9Sbym3h52PfMk6fLV+LxeyosL6WxuAkCOjkS38lOixo8jaNq0QUnZ50oHTbdE879HlvP2or/R9PI8kh/ul2Hf/eab3PngTxDMKpa+twX9iOnk5kWzZsU2XE01tK4/xMT547Dbu6mrc/LMM+2IYhRJSTB7NgEj5J9AwPgIECDAfzQarYrkvHBsHU4qD7eRMjJigBicz+7i5PJd6ENMDL977nkrjmp27iQoOpqad95B2L0HJk8mccGCC67B6/GgOWWgtJeV0+iQcK3ciM8n0VspExmnQaPS9BkeAFkzbuHwznUExaSRO+Uq9AYjK9/4HWPT9gMgO61oYrN5c++bPDT/BQxaA6Wte1hV+hfUgpqinjquiLxu8GIUBVPQQGMpIfsmbHXHiBzhD/F0Wv/At59cgNUKMTGh7N0D1WXwzDO5/M8j5bgdXUDKkNda12Hng5Xl/OiekehCQ7C2dxAScf4WAOte+ANidDQ7P1uBWqslddhwckePvajqL1FgyKqUBfH57G45jk5npjz3blJOeT06ysqQJZmgGL9RNC4rE40osuaZddQmxXD/D6exYdlf2LnnID9/fAatbbF0denweRTsvQKbNsHddwcMkK+bgPERIECAbwSWcAOGIC3Vhe3oTWpi00OoWLUfb3cvWddORm08f4lu7d59eFwubG1tZH3nOwjf+x41K1bQeugQMWPHnvO4lsOHad29B12Y3+uiDQ2jJSaW4SPTSI+LoKWqjujUwTkFlrAoxl9244BttsQ5xE3vV717d/e7PDz7YQwafwglN3oSudGTAKjuWT2go+xp3G5p0Ll0QRF43F19P69aF094OJjNYLFAaytUVsKOHfD/fhFNc8VOklMK8EoyWw424gYm5ETgcEvsLG4l85Qa79hpk9n16WqmL77yfLeW3OmzweUiddasITVSzodWEHDLMuqzEnNfOrSBgnB/99zQ4Cgaao4RnzycxmPHmP6dBwaMDSs6Sl53NUvN4ZjXH0Wsi+TnP52Nyx1KV4eI81QPQVGExsZLWl6AL0jA+AgQIMA3Bo1WRdqoSFx2L8f+9ikpC8diTok753ifw0bHsc8pripG1EQwccFdGAwhfftz77uPwuf/QPeJk8iSj6zbbkOlUiHLMp3HjtFVUoLP62PEdx/sO6aiqRNVYSVNnT2kx0UMaXici7gYDc9teJlki9+TkBCW0Gd4DLpWUY3b50WvGejlsFldHCtqIT07HL22/yteowvG09uF1hzKhqYodm1upbPTiOQT6ekx8KcXTuByxhDjcNPu0rGntI2yehszR8UQHaJn2dYawkL03DorlU93+8XX1CoVKsDt9aLTnFupN33qVNpPnODYihWMuPbai74fADoBXLI/D+Q0bp8Hh8bB4uS5AHR2NzOiYB4n129A8noHHO8Qyyk+aqDg2in8ofUkb3mrKSopQEbCYZdxOvtzhWR5sDEX4OshUGobIECAbySe+noUtxtdejrgr2axVxzGWr8H8HsHRI2B0Jy56EKT6O6uo6npEB5PD4IgkpAwmfDw/tbuvY2NNO7Zi2TvRVSrsWRlEZmfj/qsh+7O4krcPoXZo9Ivec2yLPOXfav4/sTzexIA3j68mSuyxxJmHKjS6XJ6aWt3cOJkB15FQW5pZJLYgyLL1Aqf8vjbP+bA4VB6rKHo9AoOu4gkwYwZCiUnHZzc3YNbLeLSmNFpRKJDDH337/P1VUiKglWWaQpVoVOJKC4X6Q1lXHnzhde84slfMvM7DxEccS61EHD3utj+8Xp0ej3Dpoxie0M9mpAIwrQGJEVAkhXWtO5mhEYgSPDf+2NFnzPFmUm9vZPZ4hrqYmbhSUthRYcOjVrD/7SWUFNVS6HBQsyY3fx55Svs3RON9WgEahEcrn4jTRQF7rgjEHb5IgRKbQMECPBfjyY+noaPnoO6U54DQcAQk0XstPtQqQYnkIaGJhMa6hfxkmWZgwf/isl0B3q9/+FujotDslpJv/46tOf5YrXZXeQmD07CvBhEUUQlXFzVjk6txenzDNquN2hITAwmMdHff2X3+8cJu2URpfvWsvlvUykoyMCnHGbzxhA8NgG14EUlqshK2kHJiXyCk2IHzXnsmMz3vt/Dux9GER85WDxt5xvH8Pkk1Orzh1QyktMo3LOTaYsWn3PM5vfXYDDpyZkwgvK9x7CUdNIZ3EzY9ZPRaFSoVSIPRc4hXKtBEAUUQcHSEs742cOYqNEgCI8R5LFzsuxTcuyVnOxQszTGQ6qhnPBWO+0nk8iyrmJv612Isg+Hxx+O06glfD4RAQUICAR+3QSMjwABAnwjEQQB9YxhREZchkp1Donyc+BXFTX2GR6nyf3WPRc8tqHLzsIJX6JD8UW6/fUqDa4zQgy9bhdvHdlAu6ObB8cv4rkdH5AaEo/ddoQTv/uY/HGXU3ZcISnjN7Q2XUVUZBcql4LHq8EhmYjSJWKUB55bURT++Ndq3n8jgshIMz//sWpIj8CIRbM5tHIT4689fxWRKIho9QYaqyqJS00bcsyYyyay/L2PGTd3OuYSHWZLCrMfGT1gjOSTWHu8FZvX78FyBIfQXtXIdcP9OSAdvS60EaORG908ffO97K/+gMqGSjzh02kqqWJ82h4+/+Qq3IIJF/7OvV6fClDwtxBSOHedTYCvgoDxESBAgG8sUZELaG1dTXT0lZccy5cl74UHDYHnC3bf9fgk3jmyGc4IhD/14k1EzJgzYFxRWxGJQYmo0FNlbUCr9htWXp+XG0dMR5JF3j6ygZ9MvYUIUxBMuIJ9qzfg3NqBFNLEkcKFxMf6qKwwIKDB7VLhk+HdrcE4pf4Qks8HN98MRwujcTrVmMxe/B6BwffREhGK29qNLMsDBMsGozBh7nze+PWLJAwvwOOwY7AEoTMNTAZOkuIpfHkPQV4dyTdlAyBJMkdrrRzvsCMBC3KiiDL3G5WvHS5GURQ8Pg/vbXmP6XnTuXX2rQCMS7mJcSk3Icsyy0M20rt9Ck/+8GUe+exH2AvN+Hwqv+ipAKIg4PF0AAM7Fwf4agkYHwECBPjGIopqwsNn0Nq6isjIBYjixX/liUOEZi4Gk+7Sv1b31Z1kc9Vh7hkzjyhTv1ZJkjeYhL2NjL32Qczh0QA4fU6WlS3z93bRtSAKIh5JwmtIZHVzET7ZjT7Mwge1e5ARuDN1MsWdrQiRGYTfk8PG52JxNBmQZDU6tRu9QcDu1qOEdZMdbWf7Zztxy0H8+BejaWpT0KjVdHUJnDjuo1ds4cXdR7h++DRiLQO9O8PmTePw6i2MWTT7PFfqN1yuffBOao+dwJgSSW1JOTFJ6aSNOqOXy5x+T8emynZO7GojRK9mWEwQt4yOH9LAKYiJYn99E2PjYwg2BFPfXk9FYwU3Tu+vKDpQ18DBFg+56RlsKg/miZ/2sr37GT5+/GEEr4GkRC1OJ/zxTwdRlHmB5NOvkYDxESBAgG80Gk0wkZGX0d6xEb0uFosl/6KOE4Vz5y/U1x9EozEQHByPTmfpe0j5JBmH99I8H9VWK28Vr+XPCx8atC9m+FjGTbmBnS/9EnNqJpNvfBiD2sBtubcNGLet+ThaTQg55iC0KgPGU7kXL5Ws47H1r/Cba+8jxGDkhY1l3PzDSrzHyvn408kYFQPDoq10tfv4acaLuMc10NyuY0/L8zgVB0mTWjB0JjEyX09np5mseDP3j43l3X0fEdewh/jI6SAIKKeMiqDuRno/OHZqVcoZfykokozncD0nQrYgqkSyZ01Ao9eRWpDF8U37eePn/8tNv3wMg26gF2RmShjNXU6izTpGxgWf8z7quyK49zEbrz8rsu39b/Gzn8H9j1QwObeehEi/PHtefDjfWXWSbv1xEkfPIu/zDlISx3HVH/byi3g1h2dMR3H20niwhM4gmfCChZf0WQa4eALGR4AAAb7xiKKOqMj5uNzNtLVtIDJy7kUcox4yjFBY+D7BwcnIikRV1Va8XjvgzzHpdcvIYjAr9m8gOzaJnISsc86vKArVXQ4UtRa9OZfnj60ixhjFTSljUfW9cSuYzKHMePhpNjz7MPs++Rs6UzAjL/N3gpVkiQM1SyjrqKOFmRwWzZTV23hm4Xi0GjU+m4k5UeF8sOpD5NZ8XKYu0l21yN07eWBSA58fvY4H5tSQnpeJZ9tklop7SJ52B5q3Ylg4R83qrW7GTNnL4f1TiIrSIssydU2NTA9WsafbS2SYg7Ej7xp0XU5bN0he8DlRvG7wudBHppAVtw/DrJm4bXZqtuzH5/WiyDLmiDCCxmex48B2LpsyMG9EFEVuHZNIUa2VV/fWcFNBPEHawY+u6mqR47t13H9/Ow6HyPTpRgoKDKz6cBlZC4bzyI5HUFCwhARjNMwhOKKDjRkWRsoSvdVHuV6byOvP38sVE6cSN+5ePIeL8XV1oQ49t2pugC9OwPgIECDAfw16XQx28cQFx7W3l+NwtgzaXlj4HtHRBURHn7sT6iT81TLL9q4/p/FxoNXGR8WN5EZbaO1xMTl6BNdmRVNha+TFktWoRA0iEF7cSpd9OwCTR9xD0KzRHNjwJhXtB6lr34miyOTFX0mQK4jIiBBKWgzExqp5fFs5gqIQKdZx9fQbeET6gFacXOPsIkrjZtITz/H4/+ygsCyaGmsRRo9C3G1XkLZjGLsagilyHcdR5EanjOCj92ei13Rj63USl+DEauvB0xjKDM31JI0cbMQ5W2qoXvMPwtOHg1oPah2CWkf7iUO4TlrJmjkbncVExoKpA45LooDHX/ohMybMQKseGPKqaKzhUPleLGojf9q9h9CoMNqcbYRoInnzRyPpslloKVXhsurZ3WlApQK9HmJj47jzW/fyzOZnSAhK4Jlpz5Afmc8L+55gdV05kRnTUTfVMayylYnqNA4ZjfhcyWgMFtSTJmFbvZqgOXMQ9ecXqAtw6QSMjwABAvxXoXBhaaOwsDRq6wa2mT95cg0xMQVERV24BbvdbUc9hJLnjoYutlZ3UhAVxNMzswZ5VdItcfxguF8UTVEU1nqGEzrS36fF22HF+vF2rLpDWNxphHujURSZpup9hIamEhGRjbeunHGpMSyOCAFgzb7NtNlaSdpZSLq3gYqqNt4q+Rldf5BISp+D16hlp5jMu8+H8coTveQnhBPXLeMe3Unt0bH4fF0EB5uw95gZk1KPKiKFcXkxkDcC6/FiSt5fjjHUjKjqz41w9XRhTh1H9LTBuh81HED2+lBphxYkm5E2m7+te5P44HgUwOVxcaL9BOHGCL4z9y50ZzW7e/99mZoTvUhdOty9/s9VkkBRQHcqF7WtrJRxpiyeuPyJvuNW1Gwjy5RDckMbqbUmosRaDrWH0dmVjj7Un8siCAKWBQuwrV6NZcEChPOIqAW4dALGR4AAAf67UC6ckyGKIrExYzl0+B+oVX6dEK+3h6ysC/d5Adhz8ggzho0ZeFpFYXVZK0/NzL64ZeKvvDiNJjyE0LvmEvVRLY6y/Xi1FkaP+Q5arbYv5+RsufW85Ot54/M/crI9nWu/fR1/f6GRyhojCdEeWqucRGtNpJhjWFlmxCDZSLs6HbfVhe/P25AmL8MSqmfcg88gqDUcX92Mz7qL/csjyZ8/g5DcPEJy8wat29Zupba4fMhrEtUq3G4PWrUaUGhsdxAVou9TYh0zcjTS6x/jM5biyw3itgX3Dzje5XLwyYq/4vQ5aTCF8vbfF9Jdn4wsA0r/dYui3/hwdHbSXl6ONqh3wDwbrt/A/1s2ibqey7midC+hC26iOsGAV5IIH9Wf7CrVlqDpPoh8oBPVpIHhpQBfjksyPp5++mmWLVtGaWkpBoOByZMn8+yzz5Kd3f+fqaWlhZ/+9KesW7cOq9XK9OnTefHFF8nMzPzKFx8gQIAAl4rBkILdXoHJdH4F0tjYfGJjLy459WxmDJvAsn0buGnyQlqcHl49Uo9aFLhzRMJFzyErg6Wuek+UYeyNx9vRScfxA5R1/oPYYVMIT/Ovs8XmpGRHCeNcuzF4Oll+2MXMkQV0jI3nUG03XSfiScnzsfKlLkZOj6SxCzZtDMIn+Ti5+RCHS/YRk5lAakEI9coovMcPAwJl2z5CYwZ9qJm4pBwO7y5FJaoZN2vYoHWLKhWSb3B/GYATYVq6SlsI1elRAK9PoqHMyw15sYQEG4iIi2fKXTdhLapnl2MHkiyhOtXT5dDG7Xg1PhLtccTHptFutfLX4nDkIU6lVoOj10NXh48/Lb+WmTOXDmgWJwgCDw77HWV/e5qMp//KRy98SqUqmY0dGt785ScsySwk3qiFyDSMt/0v9LZA5VZIm3HRn1+A83NJxsfWrVv53ve+x7hx4/D5fPz85z9n3rx5lJSUYDKZUBSFxYsXo9FoWLFiBRaLheeff565c+f2jQkQIECAfyUmUxotrasxGtO+tlJKrUbLsKQcFq/extUJqXxnVCIRxksr3XVLMnaXD4fTTnVdGcfWv0B+uBlNaAGdLg3G1JF4fTLBsf1iXbfPGgnA0b+/Q1cXDLNE0lTewuy4cCKTVayyCbjtDj541Ua7K9V/Hqceg0qie4KKls1u9nkauHJBKutKOrht3kiWv/Ir9KLIiIIJJI6/AoDw+EQ2L93K3k83MeHK2bTXt1J+oBhREPF5vYRED+5yu6eugZ2tLXxrzCgSw/vLdJccLOTp3Yf5jiSi+GTwyKRePwVNeyRLlv6RcRPmkZU0gt7oRqzHWxmdnIZNo+PggRN4PBNPzXI6lOb/PF0uBbVKRDFEsXO3QmaWeUC32tauDnaX1xEvZOLYWswuQyzV9Wbqent5/apc6l0GXKFh5BVM8E8bFAM9TSitpQhRZ5QEB/jCfKneLm1tbURFRbF161amT5/OyZMnyc7Opri4mOHDhwMgSRJRUVE8++yz3HfffRecM9DbJUCAAF83Pl8PVusBIiJmfa3n6fU4+Lh6F1ckjibS4H/gftDUSa3LjYDAxBATk4JNqE7lfixv6eREow3R7Q8NmRu62ed9nR8W3MmPdz7OfJ1MbHUkw1NHMPLWx4c8596P1hKUGkaPzUpTaROyJglBAK9XoqEnnxf/EkRPhxdJbcRsVli00MknK0S++z9/QUMo46ZOosWjxqU04rPtY2fvYf52/euAjy5rGT6vj5CQZAz6cFb/4x9ERKQRHBlBzpT88wqMLdl/kJvHjTnvdsntRdSq+4xCWZbZfXgdzc3VNOmO8+Ds5ylet5TPdx7DI8m8/sH9VFcmAAKC4M/1AL9IrE4nEBnp79hrDu7C5zXhcYu8smol0cFRTIgbRU9VDzHjo1n70kHWuF3MyDLycdvfMKmncp29khnxoeim3Yjn4OfI1jZ8XjOmq+5GZQ68SA/FP623S3d3NwBhYf7/VG63GwD9GZnBKpUKrVbLjh07hjQ+3G5333GnFx8gQIAAXydqdRAGQxLW7oOEBA9+IH5VmLVG5sfnU9JVg9qlYX27jfkRFm6KDUNRFDZ22HihphW3IqMWREwqAbVH4UcT/d4MWZYpfV/g0x1vcEXPCAxhk0m7LpI1H75G+Y++S5piIO2RRwhOSkKWZTYvW4HnWC3jrvk+9VUVVB8oZ+TlCej0IvicZLRW8PmaYezu0hEWDBMnKhgtWlwqF5bZOmSvnZr9O9B4g0jw7EeJ6MSaMAaP18nxkveIjZ2ORmPk2LH3Sc9YjDHcwqRrL1y2fLGodP6kzrZeO5tKy8iMCCUxaQw92kSatnXQMraN3BmLaO7Zwz8qdhM+yUNt1ZPIipozX6PVaoHERKioAFkGX0cwIGAwwG0z/V11JZeXmq0lxI6PYeaCDIQ3j+DYvxV7Qi1H+QsLPfPQzn8A1+q/oJ14DWJ4PD0b99D59goivnNLQIDsS/KFPR+KonD11VfT1dXF9u3+UjCv10tmZibjx4/nb3/7GyaTieeff57HHnuMefPmsXbt2kHz/OpXv+KJJ54YtD3g+QgQIMDXTa+9jPK2vTgkBQUFWVGQFRlZkZEUGZnTPysICFxMrUyvJNKmziJer0UAut0Odnc7GBE9ke8mR19wTb/fU8n/TOwPpSz5+c+pCKolfOEUut3daFVabg++nP3ff5A5Hy6nftNmbDU1NNhtTH3gQULC+2XBu1ps7NiykiN1O8nMS0djEvD5bBzdoOG1tx8kLzMcWYbicjttdf63+fVr/8ac2fciO3s5uOZN2ppqkIKOccWdn6I+VQLb1nYCu72J7pYqzPoY0kdeWIzrXJ6P9/cf5Jaztv9q8w6uSk1EFEU6HE6Gx8YQrNfx55PrmBweT9bWH+Oe9hQ//fTHbHrt17QenoyiqBAFBQGF2HgRp1Okvd0/n4AMgojZDKffbyWPj8YdjSTOTsLnctBZUsTuD1ZRWd9Gc2Ie86dmElbYQ1KqEUWWENRqzDPGobZYcNf1YMj+Ev17vqH8Uzwf3//+9yksLGTHjh192zQaDUuXLuXee+8lLCwMlUrF3LlzWbjw3L+Yjz32GI8++uiAxScmJn7RZQUIECDARWM2ZZIghrK5bjMR+ghGR49GJagQBRGVeOrvUz+fzen3trPfgGVZ5sWyPSxOmdy37c5LWJN8VjHOcZWV2VNvYMbIq/q27bzyarJn+itv0hcvBiD2wD5aP1iCPH0GYXkjAAiNtnDlTbcjbJRZNMe/il53N8Hhb7Jmlxu7x0Vujki3rhnwJ+B+2Pohx/+xhDsbw4nscMDUHgxhGYhi/3VGRmYTGZlNb2gOdSf6nwHnw9kjU9rUQWZ0aF+YSfL5cLpcg8ZelhRPeUk70SnxzBnW/zy4MXkUqytWkpiWR1DNSh7SDiPp1jWk3b8JS3EFoy8fzrjb7qe+LnyAmWg2ebE7tXh9Mp+tq0GvVzNnWiL2GhuF779JdFoywdk5pC3KIrQuipbqGFrKuiBIJHlyBmHJAwsm1ME6vM12NDGB8MsX5Qt5Ph566CE++eQTtm3bRmpq6pBjuru78Xg8REZGMmHCBMaOHctLL710wbkDOR8BAgT4V9Dp6qS4vRjwd181qA2YNWaMGiNGtRG35KbH00O7sx1JkWi2NzMlfgrx5vhBc/3xxC5+kD150PZz4eispaz6MPbGIvZWullQUMASOZQ6ScdqwcjvfeuwCD66TUmEaEKQ95cQ19DD2Cd/3TeHoih09bbz0rs/JDI2k8jQJAQEFBS6nJ3IMUEcd6pIDxved0zkLpnY2DDsqjKijH4lT1mRqV+zksyunTQZQ4k0y3TqVQQJPTjGjyM5PpHQo1sRUmew95CKX/5jPj/4RfspvxA09Uqsr3Fy13DzgGusqu8kKVOHIcVA72EXIbHZ1BWWMP+KUcTqDEhuL4oiI2hEtMEmGtYepiknnYrqLjoSTWBUU9JVTbqqhZ+MuIryo6vpOlCDz7kSe1wBnza4qKy8nXV/GYbXq+fMzrQWi4DT6Rces9ngs7WVLJrv9y7t+HQ5IZGhoEBUYhI7lj+H0tGJ0XYFWcYk6n3dTH7icjS6gTofzhOd6BKDEI0B/Y/TfG2eD0VReOihh1i+fDlbtmw5p+EBEBzs1+AvKyvjwIED/PrXvz7n2AABAgT4VxOmD2N6wnTglES4z4nD56DX00uLowW9Sk+ILoTM0ExEQaTSWnkqFDMYrSDi8Hkwqi+uwkU0huPzOJh4+WOMdLpAraNnxyrmaVwUiEamtNsIHXs9K046qW89ibe2B2drD22/+yGWhCTUOgOKImM2hLA49zpW+A7ynen3DDrP08f38P2siX0/V+q6kEWBjMT+Co4epxehxEeDyos1I5QuXSjbO5Zyd3cFFV35RKfPpYxQ/vzCXfzsZxCx5CQjUyfzzDP+SpL/t7yYacMjuXFh/5yyLPPhrrXcPNUvnV7cvIW8iYnI4+L58MXDfPBBAi/9qd3vRfIpuLt6iZyYhdslEpIUyrDcKP9EKTE8emAZddX7cdRYiWg8jEk/ileqokhwaXn33SwUVIiigiIoKJL/8/F4zroRZ3xsU6+8pu/fBzatITl4JCmjsmiuNlBSWc6qYCNbVx3ml9eOHzCFPisU59E2DPmRCGIg/+NSuSTj43vf+x7vvfceK1asICgoiObmZsBvaBgMfiGejz76iMjISJKSkigqKuKRRx5h8eLFzJs376tffYAAAQJ8DQiC4Pd4aIxEGIZurR5jiuFYxzHizHGD9i1OyOFPJ/cQ1Gd8nOlgFpAVaPe4CdZoWNpqxyoJxLot/Oad+1AnT2LsjG/jEdREhSdx8/BJVK5rpqjXwhu1NialZZBaZiP+pisQw/VMHjMKgBZbJSX1y1GpjSTbBNaefJf5WQMb0AWdJbqaEhvE7sJmMhJD+rb1WJ38tQbiPFcT6ShlbvA2nC1a2tW3ofE5yYjJY97vzJTWQJBZQVQNfPO/alQML64t4ki5mVEZfl2TdUe3MitvcL6HqBIZtWAMH3/aQ9z44QP2dfS46KppZ9L4gdooUd3Z1DcdofFoM+nuXlrahjE8SsP+kkqcNjVqjYxH5UOQBBTJH95xuyE4GMJPVwCfw98/dvYCJEmm+u1PGX73bGKOxWJavoNbQ7U8YncTbNL1jVVkBSVSRcu248TMHKx3EuD8XJLx8fLLLwMwc+bMAdtff/117r77bgCampp49NFHaWlpITY2ljvvvJNf/OIXX8liAwQIEODfBaPG6G9rPwRxxhAeGzb9ouZ5NFthee1+CjsqOZF2G8HVtTS9/xxT3PCEewJi6zpqWjTsrm/gfnEro+o7KIuJ5UDbNhakX8fa4t+gVQfh9HQyNfO7GLUhtB16jnXHN2BUxzEtrb+cOD84gj+f3M2xnm5eHrMAUavGafOw50A9LqubzMRg9KEGovIVGnpOUhzbwDxzMvO1Fpo+2slvje/z5ONdGBSF2dO72bZNQ01dBM887wCMALh7uvnB7Azae9w8tmovURHhpFSvhZ7NHE+9GY0aOm1dXH89WK2QmwuoBnoOWrqcHD/SxPQZKQO2e3w+6nrsEJRL3JRclheP5tMVGdRKvTgb43B5dCgehbPl2bKyYOLEfpGx86FSichON7IsEz48hanpsXzrqSd5e72Jy6ZMGlDPqw82g1GhobiQ+LwvJkj338qX0vn4OgjkfAQIEOA/hbKuMgQEMkIzvpL57tn+EgfLPuW9Kz+ivdPJS5vLefe+iby+Zg86UWD+oYcITViER5XDjvCDGCglPOdWVB0uNFojdc4uSg9uImxPA3l3PUyLtxO1WsvMy+4dcJ4/lO6i3ung9wX9ZbKNPS4eeGs/v8pPorJwB6W9ZYzwdTE+bgSqYBurqi5jb4kZrXE/LY0RNDbkYQk/TkTENHTJ5RzZr+G3oz7ikyQr8clRPDT3AZ5c8hpSThLX1W+kpU1P+uXfxivDX38LjtYwvnePlWWfm9l0QODJJ7tBEFhW2Y4lREd8cjB35MUTauj3rPy+tIrWtnaenTYOp93Fs0818PIbcXR5neA04LPrTtkG/mwXBNDpvdx0o26A4fHpmoq+UElI6QHiwvrzU7ydXRiTE4i/clqfbsne70zmiGcckTPGMiYuCltdLSpRRFCrEdRqglRaoqZPRxN94WqmbzL/NJ2PAAECBPhvJjM0k7XVa78y4+P7wxaxtDGE3ZtqCTNoSBLUnGzpIcpWRURSNweiL8MIxItm8nyzsSqJyI0qrN27sbuKsW4X+dbjy6gavZ+0iBGk1DVztHgNq2r+lwX3/BLVqWZ3P8yZzJ9O7OKZku0YVSKbD3r58axRzBmbQER8KMeK40nEhik6nR2SSGt7C68dPkBeikJqkIW6Oi8OTwThllBEQeT712XxZFEbscWfMUEKpys5h19+uASvoGFkZzPmuBmcKD5G8ef16IMj6DyuYvKoJswdDeTHhLJajmV4ooq4rBQum+1PBHV4JP56tA6fJKMWRSq6Hei0KtrcblxuNwd3FrPrYDpuh4jaHYrLpaAoAjq9jNcjoNU5GJG/H1VLKDBqwH2+ckG/tP7ujhLSbrn8vJ+LkrSAKyZN4cj6NehyrmPEt+4fNMa2Zg2q2bMRtZemZPvfSsD4CBAgQIAvgUFt+NJzHKz5GLu7DYCZnk6y4nKRCQJFy97n/sbsq1MIixpN8NTvIkluygq3kVRwGUksZNeyzxk183dYwizU6VbS8tePiU5PwuvtQJuVzMSR/4NDdvPZtqWMGT6BhKhkAG5KymNXex3XJg5Had1MRYcdCYWPXi9m9uJc7K1GjpeUcNkV0/lw7ToyTR4s1YsJubqI0len0tTswW4NI8m8jU2rU9AZYxAjZjJv5AQ+6RJ47vbLKT64A0NINLt2vkyMK4iMiBDqnR0MS3DRapxHWPNqHDE/oiAXduz5nGE+gdycJFSiQG2lisKXUnj9dYWXiuqZERrOjZnRnOiI4u3iCnZ1t1PYkIJK70N2iCiK/3HmcZ8KuQgiVlsUOTl+Y8bl9WF19RATFDrg3isX0Whw4uO/ZN+LzzP+hluIKhg95JiguXOxrV2L5fLLAwJkF0HA+AgQIECAL4iiKENqgJyL7Sf/jsLgTmgalZ6J0fNxli6BJCtKRwWaqTlMzQlBbclDkxOL1NJ5+qwDamwyxoxm43sH8flgXFY02rlphE8e2G3WgomrZt7A5j2foxLUxEbGE2OwcG2iP8mzsNhLiFRPZlIwGRNi6S3pZuSsPIKMQbh7fHhMocyd3sS7fw5hzdOLyC7QEt3jQV9cg80Wzu61+zmujadz9jgK62QKJTMfvrIco0HAdOBzUkdPx9h9gJKPfoyhuZvhl0/j/b3zuKH4ZkbMAa/Xh8sZzXfu9DFu3Ees3zWKy+Z08enqAnLm2Vj4vA+vRsMfqpvR7+2grttB+dLpdJ7U4/WC0NfRVujTX3E59LQ0DCMyVCA4UuLRNX9iXNxImu3tJFqiuH3UbP/dPK1EdgHGP/ToefcLajWmyZOx79iJedrUi5rzv5mA8REgQIAAXxCPtwdP21K22YsuYrRCdsw8oi3+EI0iy9gOPQdOK+rCZXSm78UZPQlj1CJ0lSvYcyybdL2AsqMES1oIPR276bUWEzZsIWfWikYlRxM62oNOFHi3qImfzfNXXniP7kbpaj411v9AniTLrN71Z667+ukBK0vWqfjJ7WP5eF0FU6cnoNOq2bz8CJ29jVx/1xXM6L6W6dOnMym/iLcOFhKRkYO9yMbMB2qZvXgK3VYz1W8+wuHGPMIt2VzpLCOkfg/t3iRi5l+PLi2emo5aMj2ZWMQeIh/8Fdf+wEv7q58R+eB3ePW1deQMm0rasCCOVaTTUO+lpTkDrUrAUxFG9MkDrHk/jxsfqODvL+bTVeXB6oAgrZ1uyYQkKWdcp4Ko9iGqRHR6gaDEFrbXNrF31v1Em/15CO8f3UphczX5MSl4dQZO7i1EkiTCXJWYHE58Lt0pE0/ou9U1HjW2sOg+r4bQv8u/7dTP3roWEtUHSZ309cn2fxMIJJwGCBAgwBdElmV2fvr/CI9Nv/DgM/hdi5lur52nyv7IIWMBOQUz0FviONpqQ3Z7uTx7DGgs+BumCbSu+5TM++7H5WqlffdKeiyp5I6Z0zffqs3VjMuPouHIBnIFrz9RUhDRzrh6wHm3HfobCZYk0jIGqk6v2rKVK2YObhdfU9ZIZVktDQ2NxMbEkJgaixBs4lBlCwbJTELTMuJj0rBb7dQ2wMyHbwegee2HuA0SDp8JIWI4BoOB8IQwPti6hjEWH3ZvDPXNh5mIxINPXU6nHEldp5lumwgKeDwCWo2C0yWg1yn8v0eP8fQL+Xz73d18+ItMOosM+EQtKkHC4dUNWrdaLaPRecmcVMRnH2USZTazcZ2ahx+G8nL/mD/u+oQfTF5MR0Mr9u4efJJEb+lqsrPH4N7yPrr5DyKY/CEaRVE4VlKNxmIhNTsZRTll5igKKKe0VJX+Ct7Cdjc5CaFEW/SD1vZNJpBwGiBAgAD/BERRJDb+ajLGTrik4wp2HGBjfQMbx7/Ebz169O09TO/08uiYqeTExyHLMoc3/xmNNhhFURDqC2lc9Rd8sh3JbERvGqitEWLSsHRvHaauOoyj84gMjiQ0Jrtvv+KTcO/Yhq5RJuaqSQOO3b/2KEFuA9YKKz6Xj4jh/bomyZlxJGfGsefTciZemUF9dQs1J+vpamihrmorV/30cXqcCsdXHSJhSjyVh8s4YN3KlKhkYtILaFr/Iimz/bLwSw8cZlz+RPLjYgD4pNLH/kMzyRxdS3piFUs+S6ZRHYIgqujshKAgsDeA06Xw3F+S6elReH7xBBT5lL9Bhv5HWL+aKUBmpoqycuiuyMferOOBZ+DGG/uv+UB9OVnhiezff4TG5nYQRHrtTnK8LnT509DmTsS1/Heohs9EO9x/vwoSEvjso42MmDTygp/vjChYXdTE7Jwo9BrVBcf/NxIwPgIECBDgSxAaE0dHQx3h8Rffk+qhqWN5iLEAPCD5EEXVoCRFjTaY/Gl3+d+up92FIAi4XC001K1A8A2cb/yYWFr31HPN5d+jtbORFns3FQc/QSsIZKVNQ9m+C/20ySiigk7b34/E4/Ug6TvITy3A6/LRW1RL+479aOLDSF/Ub1CpT1XJJKREow0NZkelwoLoKt7Z9Bbh7bnMu3UmzfVbKdx8CJd6DZqeCFyeEQiahr45nPU1VDp6qSg/iV6tRolRUV4O0xclsW5dJ9feGsofnnPTYdXj9YHL5b8figK2Hn8p7LlzQwfeu9pakHzQZZcpKABJghUr/AbN3XfDn/+WwKoTu4ivhvvu7e+807y2zj+bRoPhxsdwLvkNctooRIMBQRC4+OwemDcsmlVFTVw1Mi6QgDoEAeMjQIAAAb4E4QmJnNi9g7C4hC/0kFGpBn8Nn9aXgIGN6xQZHL09mM6KNGw/0YZG6z8mKiyOqLA4SMylqdvKkVdWknfTfMSwSHR6Pd22bjRaDUW79qJoYMykqeiNfoGw8NwwupZspra2naJ/rMfe6SAoOgjC+1tpFO87idLcyI6RT3DN7DSOvr4WySdTV3SMBXc9irV7GpLkIDhiFjWF7/cdl2rQMWX6NADa7XZeWfUhLUobR9enYGku4dklqQTp7Hi8Mf7sDVnmtFiY5Dt9Py7u/trt/mNtrXpEEaKjobsb2tthxw4w6/T8bNpNPFv7M87X9k8zeTHuda8hmCwovZ2kt7XSWptKVFJ/mK20lD5p+TNRq0Tm5PoNkCtGxAYMkLMIGB8BAgQI8CVJGTmare+uwxwa5i/dPJVKpyj+xMTT/74QZ45Vyg7T2ONAqm9FCDIhWoz4PC66Cp1oE1pxhHdgjPbrhU/KCGfp9tpB8x0rKyf5tnl0Hq6ht66DEaPHcvTwPiSnl/FzZ6NWD3wE2LcextdiI+eOWSiSjNqg4/iS7bRVFlG8rwiVWiQmTCY5tpYuWyvrd/UiCiY+3bEJtdjA5pp1KFIvUTvX44k8Tkd9CW84s9AYTdS7FaacOk+EycRjN97DB1vf5Pdrgmlsmondo8Ph0XNafl72DbqcS+BUsigKsgzNzSCK/R2Dj5fI3PdgDW4WcV/iP4ib+60hZ1EnDkNqqECdNQ5VeCzZskzRlo8GGB/nw6xTMy0jknUlLcwfHvNlLugbR8D4CBAgQIAvic5oJHH4KJLzwhEEf5KoIApf6m238p1O4i6/HNnrpfW554h5/EcAJF0DztYuOvYXY7zC70kwaNUEmwZ3V+3s7mbu2LEQG0N3bROVy3cyYtFEBJWI7JFwuNz4nF7MoRZEtYivy0H0D68ZMMeI++ZhXPI3Msc/iKupmvYTq5iWKeCqLKMBGzO+88NTI+cDcOJIIarx+cTlZtFdcgJqDbz5chLyMD1RDzj54ysHEUWBtPhoPnrxLixqhdC8Eo7rGpFqUpF9RrxqHZLry+ZKKH1/K4o/9AJQV+9j+swexo6OJDg4AsG2u+8I4eymL4KAbtJV/T+KIl5JxueDm28+Qx7+PAQbNRQkhrDlRCszs6O+5DV9cwgYHwECBAjwFaDWqFGpVF95h1NRo0EVEjJgmzbEjLOxncp3VnP6IRspwIY1r2O9ajEC0LxxNaNn9VfEBCfFotZoaT5wnH1F+8kfOwW1qEIURSoPlqLSgqu9lnFnn18UkXsPUrvpD2gtkcROvZ+e1UexkYezevAjpPtQNekGNZ1HK7AXN/H58Fl8Wt9BVrqCxWTg1kV+DYyHflpCbLyDD5ZoeeJ+Lw32FI73hiErAnwpr8f58bjVtLeFsmu3wDXXgCFrKu37PiVi/JU4jzXTa32B/vCO/956929EmHI1xtBQQtvqeON1O1lZJp56Cl55BXbuPP85oyx63D6Z3RUdTEoPP//g/xICxkeAAAECfAVEJJhpr+8lMinoK59bUKmQ3W5EnT/ZQ6XVkHn/QA+FvHIl2TffSGiOv5V9a3QYWz9egjx6TF8OiSk2nA57B4lkkHWWENn2Hb8iPHWw9wQgpDub6Nk/7PvZsrCAHX9dxqKH/CUksizz0caNiLJCkCWU0YuncfPNUFncw4k/mPH5BGoqzRjPyNiMC8nB5qomY5SWhpN5eL2qU54HAZUgISlfV5WIgEbj73QLEJI3k+YPfgbjr0QKT8V8092DjjhcHoprr5FEIjgyOoOykwJjx0uAivHjL2x8ACSGGXH7ZA7VdjE6qV9l9Vw5I990AsZHgAABAnwFmEJ0tFTbvhbjQ/H5EM7TM6Rp9250ISF9hgdAVHwCM2+8lQ1L3mHerf6kygM7toEkM+EMjwhATc1ejMZ4DPYmWg98SGT+YjjVOA2guUtD8yvr0Ft0fckrbq2Z3+//BEOQCa/PQ/XhEk5Yo/hunMBvfhpPeEgUUrydbm8Q06ZBSYObziotp70KmZki77yTRmOZjNd7qrLl1L6vz/Dwn0WWFNQqGU9nK11HjoHko+q5b+H2pg15xGG5Hs+4KdQ5KtCjsL3JhnVrPjfcEMSBAxc+Y0e3HQSBSLOGTruHw7VdFJxhgPw3EjA+AgQIEOArIizWRFttz5c2QBSvF+GMiheVxYKntpbDr75GWN7ws0YLnGxpxpyfz76lS1mwcCF7yppxeSQEFFqwsHLZJ0RYjCSlppGQPrAJ3uHDr2MyxVIw6l5EUaSzeBON+9/A3nyM9MufRmUwEjQsi7Tb5w047tja9Xy/YC46tZbW7mbWJp7gshFJHC9fwUnfDHLi29m120R8RgdVzQKalAaspTn8qbAZgN4oWLEigaErWJRzbP9qkBUBWRYRtAY0YbGoJ9+F/fP1SA3tHH30B+T//g8D8nXGxaXi3LWHMc/+D+1bV/Lrux289Bcjc+bAGfbekPQ6XPz83S1cNSYVn09CVhRcdjtP/nAi3d3CBXNGvqkEjI8AAQIE+IoIiTbSWGbF1u7EEvHFG8617zxM0OgRfT+H3n47bS+8QOiwXNKvuYb6qk7MIXoMoUZePFSMKjmDazNTGCkKfPDZBjrEUO6ePQIEETErns9Wb8DW7USQIQFotlpxdR+noX4TUVHjyMzsNyzC8mYDs3FZGzn52SMERY3CpRlcqiMfPsJRZyUAvbZOdHIojRY9upBwtFEtNDbnooqoo/hIMiMLnKhqopAliUfyE6kpr+CWbxlRiQqSAihnGhpfv+i2ooDHK6Ixh2I+5YEIetB/vwufepLespMEZflF2nw+Ce+fX8CcK/CbD44QZE9htjmDex4s5MrLCi54rp+9s402p8LlE4b1bXv/fS9abQ8JCRby8y8ubPNNI2B8BAgQIMBXiEototJcihzVYGy1rcQMy6L4H2vRmPQUWQ8SX1tFangKO//0R6os0WzQZ2GKV+ENtbDK5uKFQ5VUzCogL38Uf1pTRG2HnTGZ8QDcdcvV3Lp6Hze5fby79yBGTTeKspcrJ/4EjWroPA99SBy5N7wCwNHdy7B1dAN+f0TrjsPEyKmMX3w9AF6bjTXrNyFbG4l2SgyPeIpfrP4xTc3J9HZq2LFBT2iMA1EUeKfoOMKSD6mt+yFGnZ0ep/msMwv45Uu/Ps+HRiOg1w+dZ5H3s8cp+e3T5P3s5wCoRIHsFR9S+MzjhLXGctSRTrswAnublYWzJNTq84eIrhufRrjFROnna/y5OnPmUFOjIS2tk4YGy0XnjHzTCBgfAQIECPAVUl9ahSVSiyiKiCrVqT8iKpUaQaVCpRYRRRVqnQatXo3erEcQBHweLyqNui85tGrDYYKm5KGSRIRjPXRfm05XIUz5wQ95f8N21usNTBWP4mQS07RwQ3QwB8rraWrv4W8PzMVsGKhEtjg9CVGn5rY8f5yg2zmC9VWrMGqDmZk0C5/sQy0O/Ujoba+g1p3Ur1ViMUPP51S950JUqXC4vRw1heOOge+F5WDY+wkfPvsBij6R5TutLB4VT03pdnLn/xyDJZzXzeOwO6HHaRryfF+n4QGgUZ87rCP7vJSVFqN7/VXwyQhaDdtK2jieOJWPWuK5Qb2LhOYGSExk6zYvc2bPG3Ie8Pd+2VRcx69vn4WcFMlHv36ax16ew4kTAMEUFMgcOPDlDNX/VALGR4AAAQJ8hegMXVjCs5Alqe+P5PXi8ziRJR+yT0aSfMg+CY/Li8PaiyRJhDSr0YTGY4gy4GpaQVuOCYP8KDWFpYzRJBA1NpO62r2oNBqemzWZ5btLqVCP5vCYLABsTiePfHaUp+YOG2R49HolltW2887sftd/sCGSyzMWU911nFXlK9GrVIiinlnJA5NRAUKjwxg2fuyAbbvaT5Jw1XVIPh+dDgcZJaV0mTwUa32kjMqjpjwMlzmJmOkatimtbPCpaLzNyy2LnsLR5ULxTOFc+R6iRkb2fpVJpwoatT+gIyoSBsVOSKQFhhBMV2t1jH/sFxRvXs+IWfOIycwm45RBOG7tHrw2L87UTE5ulsl3l7FEjD51hlNnOkNNbtuhcsYMS2b5r58kLSebfcezyciQWbJEZPx4HZ9/LhEUFDA+AgQIECDAl8QUEkR0atwlH7fq1SXMvHwC1cd+j268F3VXJOX7PkSnmUWI3oJBa0QbbMTn9fB50TFmdjVjCFVx014bU4N6EAUVztBiPiptRSP6H9xHW4/z1yt/hOlUKEhSlEFf+imhuaSE+rMeN1Wtuej1+twuNDodGp2OaKMRW2sT90y+huIDHyFrMynpbqeyxEvL8u/y3rtafCfe4Rc1I/jHkv9HZ0cTskpg1GW1HN8eg+TToBYkPD6RjOHdNJXrsfv0yAp8WS+IICrozV4Mai2XpR/DGZVDb4ODILEWrz0djWmw9yU+exiRyWnsX/kxxVs2MO+BhwA4Ud/N3LE/5PD/Z++8o+Oozj78zMx2bVHvvcuWZFnu3djGpncChBoSINQkkALkS0JJgCSEJIRA6L0bMAZjbNx7L7ItS7Ikq/e6K23fme+PtWULy7ZsSkgyzzlC2pk7d+6d5fj+5r1vqS+hx1hHbPYMpswcHLJ8OHT2yYfqyPhgNa1iNh0OB3H5+YSWJGH270SjGcPLLws8/LCbxx5TsFi+/gip7zqq+FBRUVH5OhlOHvUhqAmB57btILQ1j7QuP9ZEibCkVKyVnyB16pF7bsOSEkPTyj2M8vmIkATC7N2821XK7Rf/FFEUuTpjKqG6I46ur6wV2bLiIADF7T5Wb6nHqXThN2oGlvSGg3o+eiWKOx9swC/3QdqxYxtqRusqV3HgjUZiYlNQUNjvbWXtggeZOuYqdKaZ3BRdy8tr1vDRezrmzIGe7hlIATdRMX7EECtOvZmGUhmLxU1PtwZFEAkxuuhzKLi8umCa+mGVcjs8OuFLvw+nuBfAr6PbAYv25hIfp2DWmIgvzqb6lecJycoice5Zx/SqMxiY8r1reO2fz/HEm8sQHO0Ud1Zi299P5tY+RpgsCGIpHekWIlNSBkTHpEnw7ruwYnEoNc1/H1SnJ28ULH3HhNfpZNs2E2azmYaG/eT9D4a8qOJDRUVF5Wuiraaa0LhTt3psKt/MGtteEmy96MjCb09i75rtdMUbmDoymkSq8a18HWt4Ila9gG7uhWQfurZ7y+cIhxbco4UHQLZTgyWrj7z0AsYDDSUHaehI5l/PRw44W5aZYGcEXFoczo4dVUOObyjbw/0PvMa2faupObify877MbFrVpKnV1B6fVx+i5Ee+0gccjJWKyxbBuNmKNiitLR72hmV18f+6jDcfgFnrylYrVbx45SNuFt0CKIMsnSoOsvJwm6/fC4oOkJM0O8MHgkLBSnaRZatkwZnPD19OvD5yLz6WnrKyyh/8q8YUtOwjh+PpNEgabRIGi0PvrGK2PA0+pxefnLeVBqWdrO84jV0YROJ9NaT1RhK0yo3oVdfx113wd69UFsLXq+CIIqDhAfARRfB229mMW1iF2OnBYv5SdI3mdPku4sqPlRUVFS+Jrqbm8iZNPW452VZRgnI+AMB3B4vCk4kxYuvZRF/m3Idj7/6AvPOnEvTvoOMKMxn/r5KalKzyZl7C7JoJRBQ8G36BGnpawN9ujrXU5+ZhCgKh6wFCigKAZ+RZZaNFNZkEybYMBiMNNc3EpoxFVmGyy4bXm2SEzF25Az2b9vP4jVrMR2U6YrJYENtDFkjJP7wO5mJZwYISJ1MOsNHVZkVr1MgrDufqio37e0mRFHAoPegN/hxOCRkHwT8WkRBRhAETHoffe7jJ1c7EYeFh6iR6fX6sIR6mXF3Gf3tTrZ8HsuPf7iHTesCIMsISQX4ex04auxBPx05gOz3I7aYsYQITFrzAT3hbjIvu5qR1tsG7tH8j2f4xT9n0vhn2L+fgSJ2isKQ5qJd773FNWOqMc8wk5SbS/L4CdS3f/Ohxd9FVPGhoqKi8jUQ8MvU7/dh79h0wnaCKFFeX4on7H3CIq7GZFiNFDWKmu69ZFp0lGxcDIkuwmNiOXNMGlahh2e3fIaZFKzGKJrDCpkZoxl44a+IjCCp/yAaSXvIAiLgaSphf53COZPPIcWUTGNHM/f8IIS61kQysw5QV2Uju7CTXz/cQuW6M75SqGe2LYfx06aiTFWoX3yQXWv7KRylwV3pYt4IPa+vCuPCs0XGjGrnlRe1tHfITJ0RoLvXjxyA2IgWRK1Cf38ifkXAZHXic2lJiPDR2q1HQD6U+fTwz9F8uRCcclTOkEOZVNMlzGYJn8/ArOJM1n4eSUqChSlnH18kHqajZDvfu6QAzdmF1Ly1GFFjoPTjj0meNImDm7ewbkMMaXF9ONoDKIqEIEBKStD6oSjyManTR19+OUV+P7Is01tfz54N60kqLj79h/8fjCo+VFRUVL4GWmvsTL1iKoYhqssejbeni9eX7WFGeBM5Kal8WLWFCF0K9IHiN2AriGP2xCN1W2q6Wwhxl/PjyTPY0+7AZnczJiNq4Ly/aTPVngBCQMHtA2tXC1OTLqJbrmRsbDBCZfXSaCYUw/JHInj+efhXFVxzXjQdfXu/cp4JBQVFVhAlkZRzM0je1smGXQJX/yKO5MkyPR8pfPIJJKXoEAC/X2DNSjMCEl4v1DSkIolufAEJEJBFhQASDZ0SsiwgSDJKQBy429AEN2gOCw+NJJOcIlFfH3QAfe45eOQR+OmPYsnJUdAO04f1+tuKeHt+GddfNZLMmy6h9oNl6Bue4JODs+kx57G23sK+/Qr77UFTR4ghQJTFTS0mvD6Zfz37D0pKpvLYY58c5ZoSHKvb28F1V9xBfPypb9P9N6CKDxUVFZWvAU+/76TCAyAgClwkLSU99jfs2ricH515P7ExUTzzj2eo6K2k4MwjwqOj38GrW3fwmzlBh0hJCNoBjmZC/ISBv1ccaGNS7liMNj267gY+XPk2I+Ku4v4HXJx/YxX/9+BzTBh/FxZLJjt3wpkjo/nXG1up6Yji/a1bcfqqOdX38MScdGr3HCCtKJgR9OZfR3DpBT6mjPVQNEFPfpaHvLEuDDYH+/dZaW8XCQ0Ftxu6uoJbFbJ8JDQ44DEQagkQUMDpFBAI4A2czC8i+ExEEWRZISnRR6ipmzolciAF+syZcMsvW4mwmMhOMg1rbn5ZQXMozbrGaCDxijO56b0ViMZebg+PYl1Aj2iMItvaQ2ltBP1ukZbWoJNrd5+Z8trZ6MMhsiiSUJMR8VDpvN6uNi475/dYddZTfNr/PajiQ0VFReUr0t3ST0io/uQNgaqnP6Rln5b2lIU0hk/GubkWi1JFtC6N0ZPmsW/rVkxN3Qh6PfNbWvjt3HkDjouiCAH5+D4CPk8AQ4gWWZZJam5BKwmsqnwTl5LPqsWtKPabKFmxATciGzdHsG79VEbkCaRGilw+LpXF+/817Dm73B4WLV6FVqulr6aX3Y0SM6cmEmoz8Oqfa+l8+zPCRmbhnyRwzhMjqWqLIULpwuWKwO0GSfpyYFDQsVT2ith9IrNni+wu66ClNnzYY5JlAIGDtQa6TH6MBoXly4+YOTaWCsinEI1k0mvQxOh5+K3dZEeG0Nvfz07/Umbb56GpchOhj8Xh9eOWNSj4QdFQ3xYUNtHmbkYZW1jfGEtauYsQ0R2csCKTFJXxPy08QBUfKioqKqeFLCs0VnQT8MqEhOmJTjn5YiJ7PHQsWsnWK75PeHEIeR3NhGk8jJ9YSP0ne8ken8hES7Cy6oZVK4izmTFojzhciggnjORVAjKCRmTz01u49J6ZpGe6iImrIl+spr07kqqKBEJyLeSOSOX/HqvhYGUpXr8H2Q+fr5FwegwwhANqSVk1le0rBj4LgNfrIzcnk/wRGQA0t/bz+bIazGYtOPoJjbJR2tVBEnYeOWsr175/M40NJnRGLx6vQsCn5bBfhij5kQPB5cigdSNqRJyOViQrwPDFR1xoF/Ex/WTHtfK7+0tpc3hZvTp74Lyjsxm90khJVcqxz+6oQnIuVyebIyaBIRQlXIuzRcbY5CQp3sKei/fQUt3Lkx+vYP3ItxmhzGDylQt4+tFfYO8xExHrIjYqjP/783rC3ZNpfNbClGuuR5QkEEVEQUFs2T7sOf23oooPFRUVlVOko6EPe7uLxLwwdIbh/zO6e00rkb97kLx9W/D2SAiaZnQdvVSt7yFj1hicu9tpOdCNP87Agv5l6Gy5PLHhQ7yGPAxSCD1uH82iQlWFl7y9u6DfTVlOMZJeG3TH3F1KWt0B/vaWCYdi4e6Jb3D9GzejNYvMOKsK/HXccGkrP/xVKpBKZmzqwNj8AT/71jtoWv8ist+FIOiInXgtks5IZMF45oyedcK5xcWEcOWlR5d4HcW7yzcQlRXDnuWx5GRupdSVSk9L0lE+HEHko7ZVRJ0Gn0tGEEJor7Bw4gq3h88JCIKCVzHTRwQ+k0AgPIMnng7HZjvi8Ol1u+ja9iGxUy874VwaWg4Q73OTnJQEwBJBw7zCeDr2dFC7tBYUhVEONztmnUXfJljwzE8YkV+Jt1vH+GvCURqjcNbtYfeSVgL1I/DWHhEbYl875rH3nPD+/wuo4kNFRUXlFGg60IPepCF9dNTJG3+J/RuaufD2PNjxGc7tGsR8LRFTRhERks627Uvx6qcg6UVmzctg4oZpXDLlbHZ2t7C+ZQ935J050M/y9xcw7Zy57HjpdWI7WzEadfh8Co/9bRy/b4jA7wdTKGyNugcvLiYWOqkpSSE+wc3Zk6M4+Gk1keNisMQcye6pkTTIB9uJuvRitOZw/C47daueQJKM9FUJeEf60OlO7tNymINtdbiFJrrq69i2KZMRCS10J23AtvxemprcJE6qp3ljDP1uK4fFg6IIhOmceHUaduyT8HqOH2YriApWqw+XM5iQTFHAEurBZu1h0y4rZQ1VfNlq0rV7MdrwxJOOPTYimX2lK0hOCla6PWxsiiyIJLIgEoCUealY11SRlX0ZFeMnE91bR1uyjfwLHuH719pZuORmxueCN6QLR72AcMiCFWg1Yp4+fGvOfyuq+FBRUVEZJu31DvQhGiLiv1yJ9eTYu/vocfTz3uPvcZYpCouYQkfcFhTTXGRLFN44E1Nyk1i1th6fx00g4AFgdFgstY4mFh4sJdaQQKZewWCzUr9sGRmzpjJx5EgAHn2ijfp6HX5vgF570Brw5N98+L1aFDmAzyOz/IswjPcsIfbsc2nZ2EzHllYix0ZjiQvOx2RIQ2sOLowao5W0ucHKrrqMRspWbqZw3snDUw/T3dfPzJFFpMRkMq0ZPp7v5CeXV3D32268Pj19FQb0kod+QBQDaDQKfr+WHpcJk7Ebv8FEXHQXzW1DL9Sixo+zX0tAVrBZBcZN8lDTYmRvZQiuHoHbbkzHbOpkwmQtENwSC7j7iJ1wCYoSTF0mKyCjICtBgSGjoCjgdHQOThB2nL2uLLeGzvRHWRH+MUmGAkzaaDT7t/LDX9mQt9pJEAyEN9QSXnQxxpRkAPrWrBn2M/xvRhUfKioqKsNAkRUcnW7Si07d4gGw6InF+MPL+EvoeJz7qjgzpQUXE1m7fSFWczQJMfm8t2EZeZKByj3N6Kx1rKl6G2/ATXHkGB5Yu5mcmBks/+Bjbn/odmreeZOMkRfg98P3vgeLFkWBHMBiVTiclrzfpQMU1m8IA8AbEFm2Yh+RHQFGzJmATbbQvKyUti4ZbbIGd69zyLFbI8PY8u4iNKlmFAMgiBze7pD6nYiSDSQNiBoESUOYSUd9fRNxuQlAMLPnkw+Z+Psd55GVUcWuPXm0tcSjyAKCIBMa2s154z3srgqloV2L3w1aWcYpHD/KxWZQSI9pobQ9imm/OsjS3yfgduqQBAgxebnv/xRe+3sznhY3TWvLg/Ov38727aGIshdJ0gWfknBoJoLIwKwMFvLzj1SrlRWF6q4q8MgEPDLV+/zIHQo+p5+ElBgmWW5mz8QIdH12miqqmZWbQlPv++SMLEIOi0FjOqp2y2mm3/9vQxUfKioqKsOgubqXhOzQ07p219ptdLVHUBeu5W9zkpCU3bgmf49uTQ/j4+ahk4yUt6wkOy2eiRlB34otG19nVMqdRBijWV75JtZ+E6/eD4/MaaPr3Q9p63aw8u232b0nB2dfFgJGPH6Jzq7Ddz2U+wJweTXBIE9BxuE/SMP+UEISKjCFWqjzrGL2edfjCShETx863anJbGL8ZWfRvfgfmK+49VDl1mA2VXf1a1iSrwfZDV4/suynud3PSH0boX4dkItGA7971YXGLvLOfC1NDXZiw+20dVlo7bbh6g/jjSUiBr0fv18BMZSATyTCZD92MEIArSQjSW7WlcSQVtDL4gdTCBgEDBFeBDvoJDfTZ9hYtsJGSDjETi4KXjv5CrQddcjuXpJTRg/7+6t0VlHQEIegl+gRA3xGC7fMGsv+8gZ2VVQT6rXSVbmMyaY44va0EpKZTVZOAeFXnTNkf4qiIAjDTDbyX4oqPlRUVFSGgc8dQG8avs/D0Wx5vwa3EEZMdwpvry8j3ZBBV4eOir4GdO4/8bO8aUTL5UxKu3HgmilRaSTbRoCiMKU7gY3CQYri4jBOuZkfPRiO6O7g/u99SmltgMYmEzp9Gh7v0ZaCI2/YgqAgCQqSJoCt8BLsvS2ExMYzMjOL5sat6FNiOFmgcGxGMnJ6GvFR6YOOd7YkEJE+ctCxBMBV5wZN8HkpcoDoXge7dphJjh/JHXM38+u3JvGH31Xw4KNhSNoAZl0Am62f5i4LJsmNotXS1j94y0UQQGuQkQQfoiTQ+Lv5vDtzL2e//QsUBHIMDRx0JuDzSix4bQMdbeOJ6FqFuHorFFwOUdlogf5TrJTb3OFGyDGTGBFPvCzT011FboKFeGsif+6sQzGFkrl4NW0Zo5BjY4gvbwBFQfYHEDWDrTcHxRH86RI7r39kO6Ux/Lehig8VFRWVYeDu9w2r3c7H3yWiMJ3kueMA+ORvryIZD9KtkyF/LNvSwohuEbmuaSnbXFm4PWexsXc1F827li1b/4SAgaSkGWikEOo+W8Qdf5lIZ2UWkeYYGBeUXQAArjJJREFU1nd4KCsPoe2gwphRIMfO5o2PMnC5DCcck8Ego8h+EMDrXUZKVxkNji46KwzYNPXDfgbrdoXw2XtHokeAYErzIRACPgIIOPa+QKC/GaUyHkv+NBrf2saSTWNYNsPPjl0pyLKITifQ3Q1ajRm84NNIhBj7MJr9tLVGodGC3yegKAJelw6tVofWHOBTm5EzkjKZXNvC5rJQ0s5dxsG3rqPfZeFfL+dg0jaROdIM038JKx+GMx9Ckr34peHlZDlMQWweq/ZsJNIUydwx0/j9yEn8as9q/jzqDP5wxdm4PQFe3ptLiFZD3ZbdtNsshOdkEP3sO7htVqSAG0H24gv4qa4z0tE/HVDFh4qKiorKSTCah2f1aG30kH5x0Ndh/pIqmpq7MWVlk39wNd2bFe5q+4i5kc2E6DNpN0RgzQog+3I4sHYZeRG1+LIuoKdnFL/8GYyM8FJeI/LHG9ZzzeM343Jr6e4Ak9HHup0KK36owzOMwmuyrCAIEpIkctk1D9Pd1kh0QjA/R0Pz08e099eV0ffhS9QrkejjUzlsRVnliKCiyklmpolPPw3WLXniHg0Bv4vPn/s9Uy+8AVtCFu27XkXY9xxi2hlYR92GJiSebvd6Olr6icsYSdO7AewuBxZrKIIAISECek0f43PaWbE9nriodlIzDCxfHQEIaKQAYVF+ulr1nH++QGsreMOcxBRF8FnFGs4/N4zS0gtY+tbtpCaLuFxQWRlFb7fIio0dfLj2Y85MmIhl5aOY3A46si4a1nd5GEkj8v2Zl9La287LK97hjJHTGGML42/lG7BqNFyTOob2eTfiae9ma0wLo1ZXIpnHsi+zgZ4dizgzdCZ/eH0uHR16Wtv09DnEk9/0vxxVfKioqKgMA2PnZuq+OPlCH5qgpatsDz3Vpdiq19ET3ka4oZemrJmUV3cSnjeSz6TziAvLJLf8MTxd+ymIToQUaGgLsLuqisbWhXT2j+LV3ZG0t5u47A+3oihHFiyPR8TpCh322D0eDXqdD0SZjv0rqKvYTUdyNAC+zl4cbwSr5AZa3SRShhSdgO2ux+j/4gX0aVnccl8Reyp7SUvSsLtEAL/CHXcI1NRA6Q0NaD56GHu/h51L3iI2JQuj3ohx5G/oMJzF3bcHLSWu2gp6t/TxzCcJJOXZmTFHw6fvSXh9Zrp7A3idevRxaQi4sUXH09bWic3ipbvXxJ/+1MZLL+txOiRKW51oNVoMAmSlxFLVNocfXz2HO38Chx1tD3Pf5hayogo5IyWM0mX3Mv7Kf+Lp70Zs2j/sZ3c0MbYobpxzFev2bkHoaSVJ0ODbs4flLX9nSlY2K8Oy6HB2Ip2XQm9rFQn6BA74m1laq8NgbUJwWLE74ggoAfwBAY30v7sE/+/OXEVFReUUMEVG4DjYg0YX3MMXJAlREg/9lhBEEUEUSJ9uQUGg0zyON9+cw4RL/84zjfv4WSCCcpOG/ooY1u+awQU/b2Vn4f3sa/oMW3wibd3N/PSpn+CvFah1ZdDRIeKXRb68oAIE5JPVOjkaAUlUSE3TMnGiQFzhOfTWGskeOyMYTjr+SMuPH3qIqPRsSi3VmFf8GqN9BEtelumlBXtbDKvKwesJ+ktU7u8mEJD48+/P4sKfljLu0unEmOKgtxHXykfQzL4HAtDbC9ddB466synZ3UmfX4+jO5H+EXWIAYlxYwW6u/XYWzuYGr6L+sRcGssUTMkOdHoQRRN/+Us8HR0BnE6BrhqJ7DQTWZFQPNJC8ciMIWcdkBXyoszcOSaYzdR+7gPs2Po+isdBWOKoU3h+X3qagsC0giP1dJ5b+i6fWQ7y801h3O5bRF9BLUuts2jo9FNceQMz4n/Ez35WTFdbDLIigiKg1/u4+nIH734Ydtrj+E9HFR8qKioqw0COK6C5uhpbqIGAL4Df68fv8eH3BlACMoosozdImCw6/P39bFu4iqbyTIyVJoo1k+jyhDA78Ale27X8prQT92PRvPe2hjfbo/jn9jo2PP1DpkwUmV9TQ3uHhoD81U3zgiAgSQqXnefl7Y+O+DmEpSTQU9VAeFbyoPYx+Tl0CRFUtYPRlYQtNp6t1Tq2LrPQ1xcsEw9B4eORw9DqZDQaI7Ywic0b9jB5pJHmZj/3P/sAPBNCSnIpTk8y69aZ0cpapo3qoaQmioPNBjasz8DtDFBS4kYv+ejtC2W/PBVtlIjU206IqwdTQhh2B3R3gyBIhIQo9PcaaG+Xyco68fN5c18TI6OOhLhabTGMmXDFaT3HEwXHpsffwuTW7UjLnqapaDK7911I7/os1u9M5ZbrPuHMf/6IjpY4BEHBZvbR69AhCgL6gANQxYeKioqKygnQGARCYhUSUqKRtCIajYSkkQZ+AzTWdHLLbWbsdpGcHIWoNIGWvAz8vZ28+sIsaupHgdhDbZWNolQP8x/9lEB2gJamMHbugl27BPr6UkH5qsIjaJ0wGmVC9G5cbjegp6ws6Kfx0gsZHPh87THiY9PqZr4oGcf932ti1d6DpFRW4qmfgNOpB5RBWz/2Lg8aCXwN/VAn09XqY8Q1GazY0Ed2sYCrN4SSvTKdbf0ImgChIXa2lufg8yrEWuzcctZWWgJjueW8Tbzxqp6y1kQejnuOf/ZehLG4ihlnuNjTtYUlS66luDiCrCy49FKBRcud/OPv8MorJ66l0+Pxc13yN59JNGzbIqxjc6g+6wz2S0YMs/Kwl4QRqIvjX7uT8R6KQFIUgV5HcNvO7xMRvUOEEf8PoYoPFRUVlWFgNBoR2hvwG0X8gGeINkuWWomyNfLQ76v44INk9u9P5N6zZnH1Pe9RIR/E5o+jvDYNvyyyarmbVSvOwOEx4vFqkLQuCGjRGxU8ziNF106P4Lu6ooDdaWDLdnnQWVEj0VVTQuPSGhLm3oDH40EQBGIysojqTWfKrens+vPvmJkbxdotJmREZFnEGhrA3hNcTJNiPXS5rPjCopg+PZmG5/ax9M0drF5owBcaRW6mibvu8nDBxVpExY3WFsJjV37O3S9fg+Lx8IMpIj94pJcb140lWSknZGwu1tsfIPmRJvb3jULnW0HDzgw8Hf1ou6pY8o6NbGk/W5Yk0WNP4HDW0sO4vQEMh7bE9rQ76HJ7v8LzG0xdWzMblrcdczzgDxB6yTm0L1zEZnca7236Ho4lLkxh/bRVxbK/+nq6uiQEQSHG1kdbrwVFAavZR5/bhByQEaX/TefTUxIfjz76KB9++CFlZWUYjUYmT57MH//4R3Jycgba9PX1ce+997JgwQI6OztJTU3lrrvu4tZbb/3aB6+ioqLybRLZ1EwgJua456uqRYptpRQXX4kkwX33baSkZAWmDpG29SNo8mlRFAEUEX9AizugxecOpveWBAlB1OBzf30ZMF1uARTo7DEQG97PJTNakF0aShZ+ylvmQqodXp7buJmrfpWKx2MkOSKLkKAfKmdMz6XOO5/8kd2ErMmk32HAYT/ia9LpMCLpvPT1K/yksY7cXC/OsG4sEW0s3DiBrRsDvPe+DY2gxy/LmMwh3P6vywm4vWgAhzGdhy9agNBXg5I7geLbJCCMqx4yctm0Tu5afwb5M8MJSxb4ySPJXHkl3Pm3TAxSPZ6AwFMrDqAQFFgBWcblk/GLIFq1ZIaF8LspmV/bc4xPMDO54NgEbD6fn/efXkZ4Uj7Ru9YzMqaE0Tds4N03bwYcpE/eRdeCmYBCS4+Fw4JSJ4HiD8G+q4rQMVlf2zj/kzgl8bF69Wpuv/12xo0bh9/v59e//jVz586ltLSUkJBggaKf/exnrFy5kjfeeIPU1FSWLl3KbbfdRnx8PBdeeOE3MgkVFRWVbwOr0IhwoP+45/M0GWzfH0H9325hZee5mKQ42gMHePfTO/H49Bj1/ThdQT+Erj4jAIIQtEpIoozJ0IXba8bpPrU8FMflkI6RFZEZswPEFmTw2mugJCZy3YTRvPfyB9zx/gFCQ408/LDCa0/V09KfzM51fwFDE1afi7MKv+D/tHegYEQU5QFfFJPkIkTnRdLHcLHbSnmgi0DHbm4ct4iVW8+hqcmI1ebD4/XjA8rrFRLGuxCqrTj7ROLmJrKlJMCY66/HoOgo/egLRlx8JgcbDVhzIpk1cT1hegezZhWi0aQxfz44+zxsWFHPnAumAceKwD0dDj4qayXDavxaM4h63EPZucDV50GTBBHpufx57Wi6EryscFjw7Lfh6glBcOaCAooiotX48QckjMYAt/+ike1brdSGrETvjcWoswzZ/38zpyQ+Pv/880GfX375ZaKjo9m+fTvTp08HYOPGjVx//fXMnDkTgJtvvplnn32Wbdu2qeJDRUXlPxrjpLH0W9LR6EPQGkPQGK2I0hFrwJWXwoIr4YaFs8nJBW+gjL++cxF9fSYUWRgQHkEUDLoAMjJerxarzU0AAa1PPvbGXxG/X8DbWUei1IbfNZaW7Z/wfHsaV6elsGC1iCfjZa7ZtYyoqItxVSfR6c1id7sL0/Yu7BYNm3/zQ8b95lUUn4GYqD6au0KIsPoomGCiodpJSH0TMyNzuP2+Iuorf4TDZcQf0NDbK5KULKBJc5BhslBxIECStZnu1nC2L1rHyBkz2bp5CyNGjaKsDH4zr5tbvt9Of2sY42Yksbuxk5LyBnbuPEBqahw1tS2MHpNz3HkWRFoomGrhnYoW1m0+yK2jkzDoNOxqtbOmvvu0n59GHnqptNNDd2gjaUQQPqGc/t0jKFzvp0brwGFzoe1tJSo6GnuvgCwH7R4BWWLfgTBkrYg2cgoru12cE6OKj1Oit7cXgPDwI049U6dOZeHChdx4443Ex8ezatUqKioq+Pvf/z5kHx6PB4/niKq02/+3nXBUVFS+uzQbsgi0N6AVZPweF36v+1AESBAFePROBa/Xg4KIPSSBh35mQJLBLyiIAsjykTdyt1ciGD0i4PNoGZNcQ+H31/DipxfSVxtDwB88d6T303+b37R/JJX9qfT6FXIu+RNzPlnIfk8OGQm99FT+mDmGsykJ5JGclIQtMR4xwkLR1B/y5Me/pMLr5w9//YD02FgMbgezL74Mn9fCRy+uo+Fs6CqMY/FSC30WE3lFfTQ12WmojyEqpYawOB93/6Cbx/5VSEyYBTHGz+wsI1O/NwdZlrH7uimYMI17H4I9eyB+WxihqTC1OIpMo58Pl+7g6mvP45OlO4m1mEj+kpPsUFyZHUub28uTO+rQayQcXj//N3nokNzh8Paq3UMelwSRzJhUxoVpmHZeE6+smcLuiiTyR2go3dRF5b5UZJxMGr2N2396kL88OYrXFqSTGXl4zQzF53BS6/KQYvyarF3/IZy2+FAUhbvvvpupU6eSn58/cPzJJ5/kpptuIjExEY1GgyiKvPDCC0ydOnQp5kcffZQHH3zwdIehoqKi8q2hSDpicidgNBpP2nbBm88j933CwY7bMWgDuBHwD4iJoJAQRWVAjGiUHqTwGD774gwik5yYE7po2RRGwKtFEvwElJPl9jhSSG7o4wKRJjONYg9VB/5BXreG0C4/Y86exj33hfN5Zyo+QzfhGTAuO4qP92Ty0ufLGeuZTJ+4H8W+j9SkfDytElVbdhISE8YBaztnzz2baKOWWreRtgovXf4QbOHdiJJIRrSbSKMNR2kI0yO8nPnrHla+7qez3cu+e/8Pv9bAb9+7hHYD6PUwdy4UFsL69dBbW4tj506uvO5CStbvIC9MS1efe9jfVbRBxy8npuP0+vnHttphX3cqhIWE8cG+D/ibt57ZUbO55Rc72fSJHo0jinP+voI3X5lF2x4zN1+0jOULp2IJhLOhrILMqRMH+hhlMfFJWw/JBt3/VLG50xYfd9xxByUlJaxbt27Q8SeffJJNmzaxcOFCUlJSWLNmDbfddhtxcXHMmTPnmH7uu+8+7r777oHPdrudpKSk0x2WioqKyjfGgb21dFqdyPKhaBJZQVYUFFkZqFSqKAoa8wHsSj9Nadn0xOhw1hjQCAqSGDjkMxFcZI62goTGali7NQSDRoe+287IB5ew+KlrOe88ge4WJ/3eEGROJECO56gaPJ6d18n0W7rxvtrFwVYPU30+SvvAt2sXf7zXSkyMhQW9Cv93RTTzd5WS0teHxmnEoY/BurudyWPHYq4L0BQWx6r35uPJyGXkhNHU9bhYsmwLGzdl4bfHo+gVmlsi8AcE/HI+6JtpdIdiMzczslODU+fg/cYuzLERbB33SzJL9jB7YoD6egmXSyE3vYM1q0LpKCkhvKiI8DAz086bjizLLFuwms8+WMXci6ajGWaUiEmn4VdfwepxIgxaA09d9hSPf/Y4Z+ecTU5qDoKwmL//OYr1z/0Iq6UWnxjBY0/9gJRsAVGno3PTZv7Y2kZqShKSKCIg0BcIsKsvlNHpad/IOL+LnJb4uPPOO1m4cCFr1qwhMTFx4LjL5eL+++/no48+4txzzwWgsLCQXbt28fjjjw8pPvR6PXr9/5a5SUVF5T8PRVFIjs4kPidYj0QQRURBQJAERFFEAPz+AKIoUL69k5rMHnY5xjJS18gWrRVJo8Xl1RBAxhAiUzy1n7POEnjmCRcdzVH09cUQHiHS1KQgO2y03XolD1zWybjRZnYaP0b+6CKcfhOiohA4jg/CifC6ILkvk6xoaKIP+xwv2XuryL/uRyiyTMDv58P3P+SfS/9KlDebnDAb/qg4ttXZmTjnTPaseocp2ZOwVoYwNjISn19EX1tPn9zIJIueDY0SGqNMwCfg75eQZaipbERwtTEut5udrXbcFg/bXWYatC0cmBPL5+9vwZh1kNL+HfS1jkPx61n+6hqcjbPQh4cTMWLEwPhFUWTuJWfQ3NTBmy9+wvU3fzd8CEVR5IKM82nZWoJ3ZylS5z7O/asJrbcOqU/P/03PQicdiWiRA3ey5vOtzBw7elA/lZWVuN1uDIYTFwn8b+GUAowVReGOO+7gww8/ZMWKFaSlDVZpPp8Pn88XTNl7FJIkIctfvxOVioqKyreFvcNNRIKVEIsRk9mI0aRHb9Sh02kHEo3pDTpWrDtIeV8FNiWCX/bOx2TyEWftISu5knGjKzAa3cybs5uaXXr+8hsjbQ1R+LwC7e0yjY0yh/+plBURU+synHU9jPJbcPmMiIqC1ebgxDk3h6Z8v5lX32riYGc/6bGxRLe141EEtny8io0fLmfbonXklNlJrhyLvczDxo6dKOE7ucL6GecIO5mQXo2c/AUJxT0kTJGJGuHFq91EUXw9hcXp9LnaGZffy+f/+JzLzltMRlwPC1/5E0+920XChM9od4Rzy88KccljMWtgRN4YUqO66dodxs22bdTX5rB1WwY1fROwpKaSOGXKkPOICBUpP1DGyys/pr332Nwb3wSKcuLnre3uJik2loKLLyY1LZYfJmQyx+piRPcHaL70XYmSSFxyNNXlg6sJp6enU1VV9bWP/bvKKcnn22+/nbfeeouPP/4Yi8VCS0sLADabDaPRiNVqZcaMGfziF7/AaDSSkpLC6tWree2113jiiSe+kQmoqKiofBvU76+hv7sX4dC71VC784oC7U0+msxOivY9QkaHk3RdAE3RZcy+dhPv7Uggwx+HLRDOyHwDa1f7ERQFEA/5gxzVlywRatrCgfoE8lprMJtF7HaR7p5QDvtxJMZ109RqQREVFP+Ji95ptd3cNeWfXPbQH4ARwAhSjzrvdTgROjVknT+G1uZadu2sxWioYu4N/wJBIF75Cc2bfkb85JsGrmls3EJ5yWe4P19M4iQb2zeO5sHXLsDeth+P4qa5fyctjQfISEzisYfeJsSUSf7ka2je2cHqv35Ihk7Hm+03c//b9zFpiob+Ppn7L3mByKxzgWMt5b1l6+j87B0e/v2fccoBluxax2WTzjrZV/eNoxVFzHFxAISZY6jbvAA0Ghq3wO+3/4u8GWdRlBNBVkooADkFaaz4dCNp2YkDfh6iKBIfH09DQ8OgHYX/Vk5JfDzzzDMAA2G0h3n55Ze54YYbAHjnnXe47777uPrqq+nq6iIlJYU//OEP/PjHP/5aBqyioqLy7yDE6iR/+sSTtityuFj78nvsq0+jTsrhrFiFX2+A3z96KWHRfbQ3aGmoSsAgOvH6DMFiY8DgqJYgL8f8HMP6EFqdCfzs3EW8sOYC7HZwOIIOpA3NYYCAUQrgIgAn8Anp6IqmwXBsSKfP6aK3somOFRuxSRIaZwLl2/aS7Y1kzDn3DLQTEKlRwtl/cDECAltru9E31hDSGktMmoVHHprI3T8N1mEZNTmZ8NaDZE59iCxRIDtqKi0tH6MofgBii2bz8Rmb0VsLueaiBfx68n0E12CR11bPgrJthG5dyJyLH8FkNQPQ+MlT6KPiSb/7KQA03sCw/T6+KidzBG3fv5+IzGBSs+IJ53NgwQf0VNZgMefTnxZBjG4T+5a7sKVE0L27j1ZrOpa+9exb1UX+GecO9BMWFkZHR8f/xPbLKYmPk5meAGJjY3n55ZdPe0AqKioq30Xq9pXQWl150naa8gBtHWdTmdnN7MYKJFsjC2bez46cTLa0jOZ9/Sz++ctyLj5v9lHC42iORK3EbnLR4A4nJdRNW18KDQ1w660C69cLzP/tWn7653BW78si3GSnpcuGP3D8cSkoTJicyZsffYpJMpFvNRPYVoo+LgZzcgzZd32f+j8/gLMtkwtvvBxBHLzgdjTvJzz5CnKTgpk+C2K8vLZyLXmuPsbNnMOWneuYPz8YLNDQfZC6zi3kxd04cH18/KV8sO0eTPZuFBSm+zYz3WQm4Oynd/U91LryaJDTiYtM5MxLzqe+pp7Na3fhcntRFJCqqzjr/DsG+jPqJPrcXvwB/zdemv5kS58tJYUDq1aRPGYMNSuWgtNFVHYuxVWbGHv1lXy+ZBF1+Glv6MUnGchtKEcTn4pW6UX2eRC1R/weMzIyKC8vJy/v2Iyq/02otV1UVFRUhkHyyELSisactF13eQM1L7xITN0BKp0WJueGoa/uJ8Vn4/mmODLGNnP2nDkox7jcHZvHo7stjUAAYgoj2dgrMmuWgsslEBEBYTnxpF78Piv23klD+8kLqAkozK9dy6j4aehEIwfefonEefPwiR76OutpWViPRlKIczXgW9M46FpvUhZVdaspmn7LwLEok44kk0jR+VPYv3YdQmxQ+WyufBaHp5do28hjxhBlzWR6dtAKXqpJIcIURXTcGBRZIXTFCkadPZtX39iLf4xMUmoSsYnxLNq+EhkFa86Vx/SXn5jIm2s/x2zUISlOMuLyKEg5fhKyb4r0mTPx9fez6fXXmXT9dXi6unA1tZJaZqesDN568yxu/sEXJCYkEhadgC00WM126eIFNN/7a2JG5MH6DYjz5gIg9vXhs1rRJiR863P5tlDFh4qKisowMISYcdp7MVltJ2zX0NyD2GdhXKcV4Ww/K8xljIsRsHblMlVJ5i/PxaEclXtjMINfsUOiHDgcFsJDI7jv4b3EpOop+cLCrl0QVZCB9+FUxoTuY6tnNAEZZOX4/6QrKMxIvACXy8vsWVPZhoXC2eMHtSldlYJuZtHg62QZz+5N+LVnsHNnMwrgbainomc/2fFx6Kwh7Nu+jNg5EwDwyDJzRv4SgEDAhyRpB/oSBAmf341WY6CrK4C7ZC3bD6ymYNxoIkcE3/TzC6PYW9ZBUX40GytKmJydQ3TY0OkXitKLKEovQpZlGjtraLfbeXPNQq6Yci4a6WR5UYaPHAjg9XoHtl8EQRj4OfxZb7EQHp+ALsRMQ4WL1o93k5eVinvDp4jMY+q0WSiiBkEUKSuDn/+8DeNNscTnTOWBy+cgSxpCxo7FkBEMC7YvWaqKDxUVFZX/dWLSMyn9v+vQ2uJQTKHIpjDQmeBQmG3QtqCgQSAy1UqPNoLirs9ZF5jKJ7pQ4upKScrchaPnkUM9niyhlICz3YZGkmns7qHc3sckk4H0i+CttyDU6kWQL8Dj1eALSCftTxQk5s2YhlbS0G/3UNHTTUavnTjbiUvTC6KIZfRkjo49qWlvYsYNN7L+3df56E8PMf7cS1i3dytP1i7l47dm87c/vo2igN/vCtauEbRoJD1yfxsblW1Mz55Ka1MdWWHTyJxlJGn8kUSVoRY9q3c002mR2FrbyPQRo48d1JfnJookRaWTFAU5CRm8tvpjbpx1yUmvGw5/eWkhI2MM7Fy1fJDrgaIohz4fJRj9bt754w0YbKW8vOgRHGSTE98KQiVsWkJ9nYSDBFpdsezoSueBlATaTaE8uWg7v7nuWtr/9GcM9/4KAGNREc4dOzEVn3z+/4mo4kNFRUVlGIiShPn8efT3tiP1d6P3VqORFUTJhEYyotPo0QkaNIJASrZCvcWL5sJXuGLrZywSGrHNG8Hahi4sEW10tcWBcmKxEGJwYzQa6OsTSYmI4EfTjiz/c6e10n/QRXp8Oc9/PpuTCQ9Jo6DTyRys1PHYY/DXJyA2p5C1FVUgCoiH3uDdrj5GnLAn8Lnc+L3BkhgTLr6C/lefI3HESM5LSGLNqhaSorMpKMge8trePif3fvAqhUmjMVgsOO0OCmeNGzjv8QcIGERaPT4K9BoMsQJ1fe0km6NOMqojhBgspEbFUtdeT3LUV09YmRQXyVlnTx52+67lHjbuuYrMMVb+8lIyzz+fzPr14J+Yx11/9NDV4mLUxFCy2crmg32cV2NmgcPLpuVbGTV7Fj0LPib0ogvRxkRTsriaJ/8W4NXXvj4rzncFVXyoqKioDJPoCdewrtvBmeFm3G4Hbpcdl8uOx91Ln8uBJ+AjcCj7KSnpuFureMmRRZMjmStdVRRZD5Ix7e/cO/+PJ72XJ6ClpQW+HGjR3+vkszcbiQnZxhsrriIgSxx5+xZAlEEe7E+iN/iJjTmy/RHwy+j1Gs7JH/xW/fmWupM/BEFAqw+ml9fodMz6wR1ceSX09FgxapxExB7/0k/W7uCRS6/jqc0fY+7qpblaonVjPcgKrR0uPF4/XoeP+24sAiDHOpotPR2nJD4AZhVM5uklH3LJeB2xYcdWvz0VTiWjil9W6HT10VSXzay8YA6SXPf9fFx1Dm/8ZhuRljH89Hs7Kem6mV3rRa71W4hJCkFf3kd5uZ8exYtu+y6mn3UmOoMJ05gx+N9uBeK/0hy+i6jiQ0VFRWWYmCSRSNnHuwsWYIiMBo4WByEDURFHC4YzUsC5YQ2OpjCSmtuYmvEFT4beTVNPDCeyWCTFeHD5TYSEwCuvgLOjh5rn1yH0+kkzd7GxKh+vX4ck+rCG9eOwG/H79Bj0PrwePYoCkVF+nP0S6aOb8PWH8/PfCLicIusPdDK56NhFWTCcfEnQGvQc6OulunQ/AVlh5RILYqieS6/bTPnieHpcJ3h+RgNh5hC+X3gGP171FJ//8vZBSSnb7W7eWXQkosigMVJp30+M7vBzEhAQBp5bsjUeqy5kyHvdMGMer6xezG3zLjvpnL4OXtuynWXN7SRYbaRnBNi91cxFe55j504jVo2Hg23JTJqehDa8i6kjtLzzZihhzU0QosclCvRGa9nR3cQlt85l3swmJHMmeXlaBJ2evRu6ePy5cF555VuZyreCKj5UVFRUToHi6EhCU5OJSc8k5FDUwvHw+LxsrDpIS9EMalpclOcnktVdxm+u+5ifPnsDHs/xS0tExhmx22HiodQiXY/vwYqNJt/HjJpj4r190wnIIrIi4Og14/cHF3EZmHKGk3ZpL2//cTw//amAJsvPR0/reOcVA+vXw4VThl6wh4PsD9CQmMqcuAg0osjqPj3nnQHd8Smcd3U6b7xwgufh9QGQHh7HvWPvYfnvfkP4zAyITAC/B5MlijuvmjTQXqsNZXZczuA0DwN/Kyxt2Mll6UMXLTUZQmixe097noex93vo7nUQZjtx2ft5eTmgKNhiLJw7bQpXroQL75pKmm0jgjmcjOx+KhpSGZVZyqcfHCDaEsLIWydT9+an3Bch0FLdSHS0my1LQwk11PLi/FA+eD+S9Y4IylfvZt26k0c0/Sehig8VFRWVUyS9eBzVO7YSFp9AWGzQJC7LAUpr69jT3oXT5abG3k+PAiM0IvE6HaGJBi7q9/O8rx1tXgdTp+1mXho0HPDx9NpJ+ANHLAAmo8wtt0isXRu0epS//1dEvw+9ZiKhEdOoy9rI2O8v5IuFc/A35CEJCookIysKvzznA3aF53Pej1azaE0Kdns0I/LsWLQGxo8PVow9LsPI5eR0+ckIt5AYFgrAqJGwaxcEMkHerT3htXrdkSyss5LDOXDhVfDG38j681Ns3fA6inbwkiQIAlm242f7LO1tOOH9LhtfzHPLPuAHMy9Aqznx2I7HmLwkmtu6Tyo+okL0ZIRb+fRACRemwvz5EFxipwFQtjmKd37jYeXyMykq1uLp2k3TilbQaMi79izi5i8j7Ip5/Pb+Ps6YoaHp81W8995l7NsHrsZYho6O+s9FFR8qKioqp0F68ThaKiu4580PaXcGS73HeDykOZ0Y9AYmhoURptej1erA7UaRZUpLd9OS4OHNHz/Jzzs2sa0iQGF+L7ltPkSNgdtuk7n35wEMZi07doC/r4sXbruJyRdcSOhPp/DxsjdRmk2E2LXMM7bQEBbCvgYIyAIanR8ppJcfP3smP7klmsX3FqCPO0i/1Er7fg1PVWxi/adRVPSE8EljD+fF5xybuXMYJd27vP5BfhAXXQTvvAO7l6Qxd/zxLTmb9tfR1dM76FjW2Hx2N06irHITtoRCsjMnDDpfVgb33Qc2G6e15VCYkkt5w94Bh9rTQRTEgSrGJ+Jg+fu4KxYyqdNJ6ZYj8/R6+9DpzHS1dvOvv/0Ao1lHVLKW6kWrKF8RRVICyP39CEZTcMzFZpZ+ZMbfEc2ePQqiKNDmCUfxeID/nqynqvhQUVFROU1iM7M5t6wNndCNJIrw5ZdjBfzeoOlfUWRycgpINk3B6ehn6hwnf1uXwpKD4+jplWlvk/nd/V6i47RMmgyP/cnJzIeXs44bCVnjR//PPnZ/9mt21+7k5xs+5aYf/pTH5zWwu3IjDVaZKelWzik4C6PBcOitWwQy8Pvhyivj+OhWyM2FglDQ0Mtnjfs4NzGfU8UoCJg0R6w0Gg08/ZKD9/eVc/uksce9rraxmSvnHVssTkrOZU+Pj8snTRjiqq9OWmwWu2u2U5xxev1Loog/cILUsYfIyPs+GXnfZ9OH7zJi/BXHnN+68kGs0T2ERgbzeOjMWmY+eDltD7zAjmdfIhDVReL2WjIS9VTtn8x7VWOwmJzMnhtCcrKWN6p8pzX+7yqq+FBRUVH5CozVObF+//xjjsuyjOLzEfD7UXxBAaIPDcPjdLPis/V0W9387Fd/xy0UkGfPIHPCaEwxR3xIfvnEAlb+34W8vmsTGxYm8faiLBQFNrr8JMddQLMrQHt0AjGpIqEBiYvTCwGFQMCHooBwqAKeKAq8996RcQmCQHVfOHt6WoHBZTMUrx9/rwckAUEjIIgiSALiUTVUBED5kiUh2mohIIp4DwmtLyfjAtBIEjqNBq8vuJALh26bkz+RFbvrWdbQQVKYGQLwk+u0bNsgotOB2awwZjz0+mQ0QjAsWCMIaARAkVEU5YS1V4rTCnj2iw9PW3yIooB/CMuHt89FX2sHgiQhiiKCKCJIAp4+F71tPSAG5y8KAogi2YW3U77zWYqm/QRR0iD7fYh+DyGTR6OvbaXJ+wWJPjtLnDPIfGAr9ieyMLv1vPJKJg88EPw+ZacT0WQ6rXl81xCU4RRs+Rax2+3YbDZ6e3uxWk+c/EZFRUXl34nP46bsr09gOlTR9MsIkgZBIyFotTj27iXrlh9jiI3jkz8+Rdw58xiTnzlo4fR73LSvfxfZ2U15pxZ7XT+X/O4Xg1wxXvv4ORIKUga2Pnr6KojW6zjiDyDQVl2FLnwqyuFWg/w1FWTAo0lEK2oPXwKA7kAbZySNQJEVkIPZTZGVQdcHUHi3uxFdogm9yYDL2Y+jpYksq5ko2XdorAoHa028/Eoiv75yGzEF2fT4RdyS5dBwlIFhNddXYE6KRFr8IQlSD2u5iU9XJ5CW1E9CrIsPl8SRnODkoZ+XElAUAgrIh+YQLtdjFX2HejrKJ+LoB+YP8NqGRkZMnzvolDBE08PHDx9zNvahjcsmzNOOxXBUplbAXmUnf1Yqij+ArCgogQBKQKZqcxeRxZFBUacoBH8pKLKMoPGgNQOKjFzZQ6DVQr+tB11mGC3d6+mzB4gNt1PS1c+qndey/d0sMnLCUBSBupoAu9/ZTsj4wVlpv0ucyvqtWj5UVFRUTpOmijLy7v45Gt2Jy9kDyBdfQsk9d2MaMYKJM8cSVZA1cK67dD3umm2IkpaoSZejsUbhXLKa7BumHdPPj6+8kX7n0c6Tx5aU3+N5m4KCY60xJ2Nf2zbMk06eU+ImfyL7V+0kf0IuO9auIr54FLFJyQD0tXbSuqGMPn8I4eERzLj98hP2Vbqqh/YegWrddF52tdC1UyYhNYq5F0eRmQnbqiA8PJSiecFxffYZ3HUXVJ68xh8APmc/5/W8Q/65s4Z3wVFs+XgV4y/IBXKPOVe5pIaYwtRjjrsdtWROTzl55+Ng9xv7WaFfSZo2jR7zPrJCR9BbmcGcviXEmt6hxXQV3rYUCqfHkJwsoXg8BPr6kcynH630XUEVHyoqKiqnSX9P97CEB4AoSuQ98CB6W2jw2vr92Pd8ASgYU0YTd85PBrW/4a7QIftxujRYrWC3H/9eihJg165XGZl/JVrN8Z1Av0xVwMex5eCCdHa2saNiK5IoIQgClfZqdq1cSJYxmarSL2ipiyJ/wk+5/BLwGSeTlydgOHEkMgAHGxrx1NcSbmpjnimLD83tOJo72Lkzkp4e6OyE8K8QZdq8cQN684kjVb4uZFkm4JeH3T5xTDSzai5m4oR8amrTqalro7q5gj3yKO78+U8YkfUBUV0bibjxBxxosnLHI1N46qYNmKcNHV78n4QqPlRUVFROE6Pl1LaGe7vdVK/ciLc1wAjfe8Te9regX8WXqK6pY2tVLkdvpRy99xHcLT++n0Nh4TXs37+QgN97SuJjn0YmsL2eswviMOgGLw/vb3yLm865E0kMpvo+Yzx8VvpPxuZci7ejlNq31/PspxVkpFp56k2B558/SVjvIbIiRxIzNgXn3lXsWfdrrr3gYl77i8i/npqMTgsmg5cYbStxkYm4PBriI/uQAybg5L4PTevXUr9uDVN+9/Cwn8FXobuyB6Nt+M87Ii8CGppo73fxXL2O3NA8fviL8xEUmeW7duP3CeTc+AN63n0Ppv2IhiaRvMvGMvvs04v++S6hig8VFRWV0yTo8ygPKSBcff00ljfQ3dSNLCvIskJYTCjjL5hA7aKDhBb8FN/W5egmnAkEBUXnnrW8sr6at3fV4wv8+qjeBjsmuAM+3li1kazuViZc/L0hxyZJOsrLP0YQjtQFEQSw2VJJTp405DXZkRZyws3UdPTjcvkZlRY2kIE0OypnQHgcZmr693lw6ZPk1rRyceFcelfEccYlQSvDSXOKHKK6qZYOrRXRlIsYGUucfQ//uOdjzJMfxWYpJCRhBPfcE0aLFzZsgMtn1bK9+sSWDH9fH7tfeh6T1cqEX95/8kGcBpJOonJJzaFPQSHo6HThcfvx9p84MiVg70fua6S+w0WZfg+lpjTcGh0OwciOrQKmuoM0drbTGJ3BM3M8tNfNpcffi9unwe9T+PADhVde+c/O+aGKDxUVFZXTJCYjiy0LVyBK5mNqgGgNeuIyYkkvyhoULdK8qRltVTkOrwVP+z4OrltGbG4W3kCAbbvL+WDh5ZTuO7GfBIJARn4hBbpjRc9hsrOP9QWRZZk9e946rvgASI81s6CkiaJYKwt2NmDTaihMDEVAxO/3o9EcWTashjCSzAXELVlJtbeYjEyBnTvh0kth27YTT+EwyemhjJhZBMAvMODR9nPmrteIefND4h4Khqzu2wdTDkXpptpWstn1ffa/8e5AH0dVtkGWZfbt3sqFDzyG3vLNbbeknTF00bq6L2pJPvPEPh/uA7X0bZBJGB1HuN1JKMn8cNJk9tcdoKKtnbh1dRScVcDesixCcwyUVIfT3ikCAiajghgI8J++fP9nj15FRUXl34jRYkVnzmTUGcmDBMZQdJbspmFrKVH6CMyWVqRxIwlLnkbfo4+wc/V6em25tG26iBhRIsbqoNZl5HhZLZUA/PzMKkCDJ9LOn/7SwKzC7590vOUVi0hJmXHCNgaDlivHBxfP3OQwnF4/e+t6aPHH8tS+z9DqgomudKLETTmzuWnaWWw1R9Gz30OhsovX1uYze3Youcf6aA7JgepSOl2N+L0edu4VCIvwsirzLs5942kW/OZcGs+IRht1Kx8tjebPf05jb+9kdBYreddcN2R/Xc2N7Ni/62sRHsoplZU7hPvkOUEMWSkYslLoensFkW4Dzh47qz2LqaGHvs5U4q+azaSJmVx2l4euBi2KInJYYrk9AgIiN1wn88prJ/5/7ruMKj5UVFRUvgJ5kxJoqe4lPuv43pUtmzeBrDDqh1cB4PzwCdbfcD2JTa1UZWXg0rt4Z14lGudIssMqERUdta3Hr8aqeEWEFh8RxQVYTD6iyp7HlTQFY9iJ37hdrg5CQ0+tzLxJp2F8ZiTjMyOBUQPHn97/RXAsikJzxx7czgCTfvAD/q5Zi9YarHqrADWLg+0HvFYUhYDXjzE+lPjxI7H096BxCgg+P0t+dSW75y8lNFSD+dY7WbRhOdHCB/z41iVcf/HdhIdDZHgaHmcPEDrkeMPjginvT5b/Yzic1tUG6eRtOOSc2ufB5NeQfPNc4r+oZcZZaaxaV0tSbiQLFoA5xE+HokVAQRBApw0giBI+n4CvuQlIOJ0RfidQxYeKiorKV8AQosXd5x/ynKerg7o1m4gwBwgRPHhXtYKi4DiwnQhBhzM6jf1X3YezrYnXen7HU1G7qdo9grTL/4Cw6zUU39DptGVFZGPreKK2eREFkfsvup7SV15DFnoQNRLm6dMPJb4SkEQ9oiRR11BCSsbk487D5/Xjdw2/ENv+nmrkQIDnX/wNeYWTmPqDcxAlkcwrTmxZOUz7rkpqPttCr78Ndy/opTTWvLsGUTLj2+nFYOgkut9J+aIEhJw2Xn37FZK0FhRFxr5/K6VvBhhx9bND9p0zcSqrXnuerPFTSMw7XvzON4PsdtDfWo8gahBFCRCQFR8IMgJSMBmZKAEigsWAeVIOstcDIX569zYTK9Sxp2Yd7348ga7GWDSaAD6vhKKAxxu0dAiCgGg04u/uRhM2jJCi7yCq+FBRUVH5isSkW2k60D3I+tG0bh2eAyUkxYehSR+NJv3IPoS5cDZ/TVjHmNBkUmu97ArE875wJh0ZCrsPZrHrg3uZnr2O1ftmM9T7tyCAMUTB6zOi13sZcfUcYA5yQEb2B1AEESUQdHL1+zzIsp+AP4OS5Q00JO84pr+AP4CkkRC8w8+eeVXmHO7914/5/tk/oij91LOHRhVlElUEzsg9ZBdfQ2fbHnweL36vF42+F3lzKOkBAxGrWvBe/gAt9Q14cnJI6NpG8sgpaJtraHr1t8Rf/9AxfWdNmEzWhMmsev1FyjetY/YPbjnl8QU5dduHw7aRMNcUFNmPIgdQFBlROJQETpZRlMChBGQBhIIQ+h21KL0H2SnM518NI/ne6s8ZNWc0+dEjWORJJi+7jb2lsQQGCg8GLTqCyYivqVoVHyoqKir/q4TY9Dg63XQ29WGU+qh//w2io41En3kOQmwy3Y0ddG/Yi8fjRSSYNPT2lFj6altJTI5jWlIixrSHaHzlUvKv6CREauKRp3/HEZ+Po10qgxk4+/slFJOCyeCj7os1weOAo6WT/GuPri0STEhmDQ+l66CPsbPTjjuPTzbVD3vOk6MyWBY+7rSEx5fRaPTExB+pC7NN+y7r5v+L827MZb8hnuuK85AmFrF+6Uqqtq8lKq4Yd+Js2gwSjgUrybnojCH7nXntD9nw/luUbVhD7uTpX3mcw6HFEY6vq3zY7Q9nVI00Teemzav55/o/88iCUHp6QNEolJZHg8YLgaAPkCAoGAwCosGIv6UFRn67lp2vC1V8qKioqHwNxKbbaNhxgPqF/0RXdA5Ntkg40I1Y1YstOozkUZnoQ760jTINNi7djtboorbsII3Gn9PUtJCVgeuI9fXQoPfi8hgY+g1cweUNYPD50OWmEpuUCUDdF8u+8bkextbl+cp9dHnLqe/YAoIYDAsWJBp7txM+YwSL3tyH3yWzu2IfxfnFTJozA/vESfh9MkvX1RCXGwkV+07Y/8RLrmDps0+elvjweobeTjsRkhBLcfGxBfSGwyvbZxLat5bv3RvgF3+cRuHIBkLa7awtz0EUFYxGAZdTYdSooCjVREXha2lBGxt7Wvf7d6KKDxUVFZWvicTiLPr0PyQzMwNBlBCE4P7+UHlADjNp7hjWLNlK3qhs+vY3cnnBXJa9E45fF8BsBtfx1ndFQCOJ2Mwh9HbuGxAfoamJ1C5dhsfeR/ZlF339kzyK0PTMr9xHhTeMML83GMJDAJQAs/J+SWt0G8wN0O5wUeZzUgzsW7YeZ78bi0lPRHkzo5xWmisqkM+Zjigd6+jZuHE9OkmD3+dj04fvMvGSY6vNDkXA76f2QDm9/Xaa6tqx2kLQG3VIGglRPPFWjEFvPI2nAJ1du/nDn6LwuMbh/Ugk1iZQWhKPxhBFwGdAkaG/Pyg69u72kZ2to1ZXyMPXNfLm0tO65b8VVXyoqKiofI3EdG2mftM+ZEk/UHUVlEE7J0dvolTu8+LWWfisvJGU5FTcIRkkGDqpEmJwOQMcL9zWFuPCHOklKknBbj/I7h2dR05GgreriWce3EDx6Biikqykj4762ucaGR3Dpv1lTMwbZlztEIiCmZGxg9OFy7JM19q3wKDH12+nzGdgY5OM1u8le8oYvLJC1vQJSHotpp7xHHjmn4ghpqAjp6wQ6O3BazETU1CIJ+An1RJO9Z7drPN6kAMBBEmicPY8bFFHIoq2rV2Ls68XAEmUiE9NY8yZk3H09tPS0IbX60ORFWoP1pOWkYJW10/WiCwsoV/tubY3NdLd3sbipWZSk4yEiaVE6/pZVzOBMBuIsp4eWUCn8SMIMh6fjrPyt/Dy8xMor9Ii2UJxl5VhGG5s83cEVXyoqKiofI2ETbmRsNKPIP/iYbXPmKZQuXAxjRYrhVPTCI+08RPtOm64w4rTY2ToLRcBZ5eJ2FATeTEwbvo1xzYphuZN9UyYmER9WRebPq7C3uH6SnP7MueOKea9ZcspSEsjxDD8tOJHY5WOzcchywEwGhh/4c343C6i928jKTabkLhjw48N4RHk3HHXwOfud98Di42wyy4dOJY4aQqpzU3ULFmMIAikzJnL+g/eITQmjpGTZ9G6rgybxYCoSOSfMQad8chcYhMH32+MJx9BFCgtfZeyXRUkpk5F0mjQaLVIWi2BwPAjhgD2bFxH7phxtHbYOP+cXha+Gk55XR79Di2yQ6HfGawd5PNrOCxbNzdPYtboRvLPSEYMCcHX0KCKDxUVFZX/aUQRUqfD/k8g97wjtduPgyAIZJ53FsKni9n5+W68go609EievXgDrxVpefPNC4e4SiEy3k10Zj+zb+7h3R0GRElAo5GIM+nQSAKiINDmCO7ZJOWGk5R78upsJQ4nlTUtx/EwCcogARAREOUAI3ZvR1NfS0XFfqwmE1XNNp5dVMiffrR2cG36I5MNHjvqmcihPcfcS1YCSA4nAFqDkdzRx1b3PeYaj4f2fzyFefq0IcvOm+Piyb/hh8heL9ULFxDdZ6DywE4ilQTSL5mCIAgEfH72rthBdFoscdnJQ95Hpw868IqiRE7RDNzOXgKBfrz9PgJ+H+jKgIknHe9h/G43MUkpRJjqWLSohYcuXMvyxrN54NURKIoAyIA4KN2Z1Sby6QOLeKP++2zcZcNQUICrpARjYeGw7/vvRhUfKioqKl835ihInQp7P4ARF4KkPWFzQRLJvPBcel5djC4+iq07dxMdF445sQPD/H7cni+XUBeYO9PIiy9q2bn4IGPPG0cgIONy+WnqcxMIQACZ6J6TZ9s8mlEWE+elHt95UVGCOT/9skJZYxO6jHTOuPh8ysrg5/cF29gyIeOGrGHfc/V7j/L+1vcBsLvtWA1WArKfuK5+Kt59D8XlRJZl0GppFUR22iz87PwLBvXha+um4/kFmIonDyk8jkbU6ci87Ht4W7oJf2oJpoyYgWRkklbDqHnjqd9Vxd5V2xk5o/i4icoCARfW0ESsoYNNI519LcOeO8CY2Wfx1wefJyIhFJ3pUu7+fDQ99qDYkzQB/P5jfVnMUgumC35IwR/fZCM/QBsTg6ukBMPXkFjt20IVHyoqKirfBMYwyLsA9n4IBZeBePLMl2OunUflx59iCbWw3lBB4frdnJ2SwIIDU1AUEZ3Gj9evQZIC/O7yl9k6PwUfwQgaSRIxm3Vkm3UD/blDuk9pyHb/icWKIAgIgE4S0Pv8KBoNfj/86EfBWi5paTDhFCNv84VMxo8bopbNl/rZvet5/A2NrAuM4Pp31iBoBSSrEWQB1/5GYn51Hf0bS3CsLsEy4+QWAF1sGNaiGLzL9tJZUkPEtXMGziUVZRDR4WD7og2MnjcRSXvku3M4Wikv/4iMjLOP6bO9t5OyFjfFw5y7oih0LakhJNHOJ9Y3yLrxWX5pyuf6W+7BHB2Jz6PF33P43gqSJoAiizS0grOjhl2tmchOB2ChzjKOx87v5I1PI4d5938vqvhQUVFR+abQ6IKWjz3zhyVABFEkYnwRbW+8TURuDckRDu47+wk+rp4EAQFBVNDp/Iiin+RZVyOu30hY0agT9nkq1NXZeXzzQdLDTVyUETVQ0XYoKrzw66v1VJV7UWQBrahgCNjpPeig/N0tw76n3+0eVju3pxfB2MUDJW1Yb7gA0SQSsDsRNAIhEzMRNRKWaaNxrN6JY9UuLIeK1Z2IsMuC+UFa/z7/mHOmSAuj5oxj15JNGEOMIIh4xINowjwUF9886Nl4fW4+27kWg06PwTj8CKCeri6e9r5AUn0uo5U52LLbiTNLhFkkuhv9GCXwCNpDu1cCckDCYPDglG3MPcfPqElTCTTtAQrRhIai+JpQDjnUftdRxYeKiorKN4nWACMuCG7BjLwEpBP/sxuekMzZoyPZ3bgdZ1ckbTn9nH32Fmp2JPHG37Zx81/iqa7LYuX6Zwl1F5MSaf3ahpofaea8CWnsbXfwxNZaQGF6agTjY2yD2smyzKMfG2htiQINpCdLtLVBUm4ktvBIcq44fiKzL7NmzZphtSsecyeKLNNsrMMr+jHpzYhRumPaWWaMpuudVcO+P4Bk1iMH5GOKA2oNOsacF8zZ4T+wjbatnxA28qeIoogsy1S1HKCsqQGvX+asoqmEGIx8uGqY5XwBAQ8znWV85JtOsakDSRZZVzuXnJm72PXcuYiyjNnops+lx2z1ozM6GZ3r5Oyrd5MvNRHd18ZvNp7HlBGtZCS6cPdB89JNxJ99enlGvk1U8aGioqLyTaM1BrdgDiyB3HNP2jxp7g+J7T2DfSHPs8c/g3Nv2soD14/mjJsuJtzaRagxgjmz72PT7t18umoVohBcNJ0+D6MKcsiKGdpZ8qQc8mrMj7KQH2VBURTmb9vHZ7sqmZwRz9zMOADuKqsnZlcitSYfP7pSy45t0NsLnZ0QfnK/1tNCqwlGoIQkhtJ7oB3TWPNx2xpHJNL52lKMo7IwjTq5ELKePZFtj7yK48yYQ/VYjsXfuIU5Fz7BqpV/ZaG3jTOSITUqkTmFkzHqTi+3h18R6c+czTkTszDViFgqWhGyo5lx7j7WLK3F0RJLnKaBgEUkNCUVAm189lYkG8s7WNFSgn3deWTNzOGJv+p5/nlYvx4CKVa8Lj8643d7ef/PrceroqKi8p+E1gB6K7h6htfclk72+Olk61qI2BPFoieXUrZwEy/+6VUqKwFBYGJREZmxM3nv5elMmTSW59yLWLW3hQ/X17GjouOYYJNTxesPkF7zGVeHt7N3y238c/9SnixdwoqqD+jstaMJeOhu7qCzE1pbg+LjmyYyOYb+XscJ2xgLM4m4bi6+hhb6t+w/aZ+6+CiqRrfitFdTUbWZuKgsUuJHkJVaSF5GMQU547CmZfLye9ejUzRcNWksezxpePVxQwiP4Tt8ugN+4rJm8L0JE4mKi6bJ18fmjR9TlJnLa8++wdhxXTSQRKc3mrqabtz+Lvo3lVFZ10CUIYs2jRa9NpjR9rCfbWJuGPX7u4Y9hn8X321ppKKiovLfROpU2PcR5F8yrOam1LPZV/oObimac4tuw/HZMvS6oReWP+5+h4VnPY5Bo0FRFMpaHJTgp+VwvZbjCZGj1sr9Pg/nHXWqZOmrxI6aS7vQwJS8c5mQNxdFUejd9j6lupco9VzPJ5/bCMge9HqBnJxG7r57EyUlQedU5ZD68fm6ycm5nJI9L2Expwy6veWgjmr5SynSD6umISI3FEUhNHV4ib2qQmU0B8pwNwW3dvob60jtlDBHxR/Tdg6hNHpaSWo1UV69g6SLcwnICn5/AL8/QCCQgUPzS/aLUfwoLpTxcaG8uHI7rR+WMv2qC7GEBbe/mrx+PthRwpfr8QxFv6uHCqGM/QE/bc4GurVhhEQJOLu7kb0GHrhvAx9pJBL0VfR2dNB+wEfJhkikS67CJjqYa3+aHfyM6v3r2bYtuNUiiAK2aBM9rU5CY4ZfKPDbRhUfKioqKt8WghAUIOWLIefYaImhuHTSA/yh6Zc0rlxG3KQCGtZVUd/ezM9ujaOnB/LyYEfHAWRXPwaN5tBtBPLirOSde2r+IH3L3qVk1REhoDGYScouIokilq79PQDuvlbyIyRue8DMLX+IpLsbcnOhvx9eeSUNOHabo+LAUkpL32ZU4U2EhAzel2nzrCF67DdTHE0RFAqvPpLsrWbfRgCiRk4asv1hSRO3rYLqj0vIOLuY6KL0gfN76/bxvRlHxFN6ey3Tbv4+W+d/jiAKjLtkHnJ4LJcWpzB8DtWcyYPtmR0kRZiIDjMBZwEwVVFYuOZ9ssZfzWJhM6095dTXNhJzsIXs2dnMf93D96/LZMxREcaRiWaqdrSp4kNFRUVF5RDmaIgvDobg5p4XjIg5AbawNNLiElhXVsJkVzJTz72Kfz20hLCQETz48428v2AkAVcIr02/8ysPLSYmlsKCGUOeS44awcq1j1FVsYEscxr77PDGG/0YDF/OQXIs2VlzgblfeXxflbD4NHZ99AJ923vQ6kMwhJpImTf2mHYxY7MxRpg58MkOqpfuZtzPzscjQ0KcGas56Huybc16cieOR6ORmHTlufR2dLPu1Q/xa60w9lTEx5f4krVHEATOKDqTh15/naSSBuLa6+mqK8Ny3eWM2zuf+b/poMzXSkCcidaWxN5NwescnX5S/FPQaE7PH+WbRhUfKioqKt82lhjIOx/KFsHIi07a/Jaz/szPeq5Av3MbxWefi909lhGJHYyc+BPcugBbtpd+LcPq79yOfcfOQccUxY/sd0PHbrKs5zHhgqcwRSXT01PP7pJFaDVaFBT0OgP5+ed8LeP4ulC+tNdkC4tlynW/4vMPFnDeFWez58Wl9BxoQPEHkAMK5vhw9OFBa5E1LZ4xd8XjaOzk4MKNHEzNYProYAK2fpcbV1c3cdOPRJXYIsOY8cPLET5ZfFpjLSuDB+838+yL8jHnrLYwfj7tLPzjPChxespXvc3u6jqajHeQUddAWHoBiqecCWMuxaANWjtk2U9X9zoiI2ae1ni+aVTxoaKiovLvQNJCZDZ0HIDIE2cEFSUts5MnsaamjbTmNuKlOvasn8iBtZ+wq+J8/FoztY1lpCQMru+xq83O6rougqnBFCRRRBIENBJIgoAkimgkAY0gIEkCPZEjsebPG9SHLMus/OxOLNqbkAPR2Pd1gdKBrMDYaZcOJODatu2D03oMYm0tbsfy4Afh8H8O/xx1bMAiIBzlRhFs1+d3Uqs3DogN4VCDvvYjSdZ2lO6jpT3oEesMCdaTiRuXibOtl4DbQ8PWg4TGmsm7YfD8ff0ezLE2nN4AESFBq8fWz5Yw9fyht80C3lOr7XI0AuA/zrnYUdkc+Hwd6/et4oYb/o+Nbz9Ha1MTi6NymaSJR2edwvNbV3Hn5KAAFEUNiuxD+Y5mPVXFh4qKisq/i5gRsG8BhGcEa8KcgMlFN/HZgRdZvaqVsRcs4u3ysVx4dyH5iWuJlMJ5sXIvob7QAV/NMK3Epi2N3JwSjSQJoOlDCOlDRofHq0ERQkiKtiGIIv6Agl9R2FkxeEnorNpMX1cFU2c8jN4y2FejelsZnn4XptBgyOvJSs0fD11aJ4bia4MfFAUUBUWRgwvmQEXgwxaMof5WqN7+HGMm/eyE92nt6OKcGdMHHYssDPpz7HlxKePvuRBJe+yS2NfUSXhqFDQHZcGBsgqiU5LR6AZvl3X02Nm95AtGTjpxevej8fvhyisZ8N0RgcBxQpQUWaE/oKErWofdY6fM42JaYQGT42LZuG05acYM4ialU3dgK8lZ4wCw2UbT07OVsLDhj+nbQhUfKioqKv9OMmZB7XpIO3HxtEhLKDemZtJWPJISRyaP/rGHXe71XBhTQLyphY12PymmLnKjR6AoCu1eP/ZYO1GRJox6Dc1lnxEeMwN/wEdJ6bt4/GOprg5ldHYyI9OD9Un2H7Ui1G16B2d/JdkzfoWoObY2jSIrX0rK9TW8XQtBC4dwilkgFOHE7T9dtRJJM3Sb5S9/hKmtkkDfOKSwsGPOuzvtmCZlo+/sYF99D60le5n1vcHRSls2b0dpa+WMyy5CPIXsogsWQHY2PPIIPP88NDaB7zhtS1fsJHViDndax/D40vvICA1DMtqRZS2pYXoKz5pL9Wdf4A60wyHxodNF0tu7C1n2I4rfreX+lL7hRx99lHHjxmGxWIiOjuaiiy6ivLx8UBtBEIb8+fOf//y1DlxFRUXlvwKDFTz2YTUdPf1cElq3kLiqCvPCZ1DKbaz81Iu78iBCwzqWbnyeA/Z2BEEgWq+lOUSkzO7kkc0HKXGl8PYXX+Cq24DgDpASG8E1Z02mucvBks17Dt3hiIBw9dWRNe3nVFRqueGGY8ciyzLCUdYanc5Ib2/TV3gQ3xwjMjPp7j42N8ju5VtILx7J+F/8lPKP5tOyYd0xbRR/AFGvRxQEqjdvpPjMWQPnevqdLH//IxJCLUw4/5xTEh4AlZUwZkzw7/HjQQgo+AKDfT68bg+7l24hKiuO0IgwRBQmRRQwb/Q8MqdfzoFdG9FIIpLRSOz4YnTdYXRWbBq4vrV1Mldf/d3L+3FK4mP16tXcfvvtbNq0iS+++AK/38/cuXPp7+8faNPc3Dzo56WXXkIQBC699NKvffAqKioq/xXEF0P91pM202o09NWWEWcTiZajGNWwgzzXTqq3rqdwxK3cecETfFizkTtWLOdvu+todPvYvGUxP86WGZ83knGpkxgx/oc4nF2Yzb0AzBmbR2pcFK8vXo/Xf8TkH5t7Lo0Vx/fjUGRl0FbLiBFzKSlZiiyfWiXd08HbVY5yCvdJT0wizDY4RfzuLzZhiwonbVQ2kkZLwY034fN62ffKC3Ru3ULX7l10l+7D4+jF7vLR3txJdJiJ0LBQAHZs3cH+pcs548JzScjJPqXx13T08/neFjS6AA89FDy2bRuIGhGvP4C938MXJU38c9VuqreUkze5kOiUYHbZtk3vkNFYRW9lMI37yElnkjXtIgA0hlZiQ3fTse0dZDm4TVRXZ2LTpuNng/13ISjK6efAa29vJzo6mtWrVzN9+vQh21x00UU4HA6WL18+rD7tdjs2m43e3l6s1q+vZoGKiorKd5r9nwRDb0/iHCj7nOx9/SZeSLyWvIM1XJ4TSVlrBZlTLiE2MRdFUbj+w8f429l3snH5UpLkJnweJ01dbsLTxqFs24bWFopJrGdzvpZqJQWtqYjMZfNpj2pk1NhYknq6+fEvf4yz1UpBroE+t4Z//rhk0Djcbg8OgwdBPOII6vU6MRq7MBoNhw6dfCvGX7uZ0AlFQ5w5OknX4GVK9vYhiCKCJoRNjWsID8lj7Kw/HPcei9esRStJzJkyGYA1b39GbHoy2RPyj2nr7u2lq+oAit8PPh8H9nXQnjOa0KbtzL7iYvr6nWxbvJTMwnySc09NdBxme2037Q4PH62ws/jxLLKyBHJzoa9P4ZKfNBBm0DI+M5KXdpZgNQVDZRVBxOX1oukq5eIUA2FZFw7qs2flT6G9jNBLPsbubOPKKwN4vanExsLGjT5KSuoICck4rfEOl1NZv7/SJlBvb1A5hx8nmX9rayuLFi3i1VdfPW4fHo8Hj8cz8NluH575UUVFReW/ipQpULPupL4fotZE9jk/4bdt25CnXIop4MFZ0YjXFLQECILApKRRhJvMOFtEhJTxaI0CZm8Ho8aNxlH7MVZXJRt6TBxs9eMIycWqayX2wD7kkWeg63Sx8rN4RiT6uPMHy1ijuYP16yH8qsRvZNpNi8BafOtpX28LiyE9euoJ27Q29KA1BIVQQ1MrpT17mT5h6LBgg81GfPGR3B9bqjcQaDlI9uTx7Nm6E0drKzMuuQBJc3rLp98Pj94TRk8PZOdEM2sWvPLK4bMCkDTQ9rYJxQN/t/c7aejrpyq0F7l9Jb2b96KNnwiCiCCICNYkbGf8DYDFi5MoL+8kLU2hqUlAELQ4nQepr8/gsceOvt+/j9Ou7aIoCnfffTdTp04lP/9Y9Qjw6quvYrFYuOSS46cSfvTRR7HZbAM/SUlJx22roqKi8l+LKRx8TvCcuG4JgCF2PFqPnQiNAZM1mej4kQRkN/6AjxWVe2jrD4aUJkh+4hq7KZw7jlGFkQQW/IboH/ydkJ+8SlbqhZxVl0V9pYWEN/vQJ6SSiYkQwzQ21E4hIrYXZ1czo/IddDUfRJaPzT/xtfAV/VTjw8dS3bryhG1sYSbCIq18smIV+2sPEJcy/Op37UqAaKGb6s1bsYbZmHrBOactPOCIk+myZTC6SGS4DyAqxIRF30eY3kzEhN9gzP4eoiYEUdIjCBLmkTcOtH39dfB6Lfz2t8sG6u2IohZFCX6HZWUM6cfzbXLaT/COO+6gpKSEdeuOddA5zEsvvcTVV1+NwWA4bpv77ruPu+++e+Cz3W5XBYiKisr/Jplnwr4PIf/Sk25ZWIruwrHzb1SkX0mrN4RFNZ/hCHxEq6uIl+cGQ1cFowGNswb32rWEjR9PTUkR7hUvASAFRKIisok2l7Atdh5tDRKZsgVHj4vOGDdbulOpPruGiL8sRFAms/mT55l04S1f/5y/YvW7JFsWlY0L6XI2Em5KGLLNxWfPHvR5yeoGPl3xOlFh5/PM30NPbAnY9gn+aVOYcM48NBoNfR7/0F+NIHCgDP7yuMALLx+RFIdzjhz+XHEARheDrMC4ccFKtMcIEEUJ5uf4Uvj19s6DXJERdHjVhWUBg/PDeJ09bPjobiqrf0tjSwI33DYCxeugqcFEVs4ECovc7Nhi4t57TzDfb4nTEh933nknCxcuZM2aNSQmDm2KW7t2LeXl5bz77rsn7Euv16PX609nGCoqKir/XYgiZMxi0ZoHCDUNXTztaDc9b9sBdnuexdY3lpnhV+Bua+OTthb+seIAiqyQtm0nXu16fKHd9L+6iubaZsZc+jh6owlFljn4+RauaveQEiHTM3MqJeVLSTUv4ZrzzuS3D0/ki99+n+npTmSNRO/+Dirc732l6SkoCAiHImqDYbXdzbvpKHn70PRFRFFEEARESUIUBARROHRcQ1hYETZr3DH9js+4kR1185kcfwO+Hhc+fHjw4pV9ePAQHhqJzXIkjHbejCtxu5w8/vetLFlaxLKNyxlXcAY28+BQW4+9n0D0LJT0QjYdVSlWYHD21MNfSV21REuHheVbg8nNvF09uNr2YM5IomOJk1fXTcVhF5mcso2c9E5eeCGL5cuT2bVjBUG/liMiROj8FNlTTleoRF3bXN55vpCnf/QM7QffYvdWJ/6YfFzu/cTlhVNR38HDL12B3xVFWNsfaOwwofglGiqi0WoVtBoJEYk1KxQEAS6+GCZM+Epf5VfmlMSHoijceeedfPTRR6xatYq0tGMLCB3mxRdfZMyYMYwaNeorD1JFRUXlfwZTOAl5FxNvjifaFH3S5rOA3fu309/2DoKjkwmGPHRCL5tcHqwjR7DZlMV5F1+Oq62NfNnH2jdfZuZ1N6HR6Ug/ZyKxjkI2vb0B9/bVTKxfz4GCJOIMWkpXmNAYdEAoAE37rsFkNhGaMryKssdDOfRWf/incX0U4wpnoCgKsiwjyzKBQOCYv/1+L60t2zlY/QWSpCc2eTxVHg9jI7PRiwZcB9vZ3r0RJVRCL+jRiToMgh4tGqr2ltLR0kJETDQ+P/z6d3n09UlEhOeiEXXoHHo2LPyELGsGmkP5MDQmHZ6ufmYXZ5E95tgquIf5cqKwhCg4Z0Kw3k35xi469XN57oUIzs9Zis7vYeTIMBoPJvGzX0wlJgb0eigqvmCIni/AY6/GUfIc/jItVjkPTXQM/YF+Usf46dEm0lWZS2JsCq+V/oP21kQ8vQk0dVqQAxpEUUYOSLgDCrIcLPx3+Pl3dv77M56ekvi4/fbbeeutt/j444+xWCy0tLQAYLPZMBqPFK+x2+28//77/OUvf/l6R6uioqLyP0BRdBGf13zOvJR5w0qNPSpvDJ84lzDx7DvQlPwdsy0aV5gFc0EsclUpFfPfxxCeiagIpEQWsOONBYTFx6G3mFm7ay/1IX68GfuIr64loVVH3k8voHnLPpKmjx64R0xuIjWr939l8XE499PRnw//liQJSZLQao9NagYQFRV0Em3sbOWVvbu4Ij+HT2vX46zqIjL+OvIyorEYjy3Ul5SeMeCz8v77Io3NkJgIbh/oDTB97rnHXOPqdtC7tJo+84mXyS8nCgtuowTpbO7it08XsG4dLI+ai1HnQy80oDlk/YmIgKnH8ZX1++HqG9Pp6XmMrOg2xP5ONF3dmBMnI+qs9HbZePTpSXie0WNKexxvp4QhQotR30mgV0evy4rfp0EOaPiyVaW9/YRT+lY4JfHxzDPPADBz5sxBx19++WVuOMp75Z133kFRFK666qqvPEAVFRWV/0UmxU3in0v/SUx4zKDjh7ddBEE4YvpXACGD+vq1iHo77aU6ijK10OLDcSCLjB9MRaMf/M+93+XGY3dSqBFp6D7IzeNv5XPlVc6ZcRZPvv00RXETqdtQS/HYBIw6DZIk8RUyM3xttNgdLKht455JZyKKIsm2VEgDd5+HT/Y2MycrmrDQYyu5iof8JxYuhNyUXub/q4UH/xFHXU0IijeA8KV06VI/WLJj6Wt3n3A8X04Utn79EWvIzi1jQCuQnQ133gkvv6zFrsRRELGRP/3TxZuf5bB+fdAB9MtRKINFTTTr10dTXZ+Kb08tsRMLWboyG8Xq4Oxb9/HqtjiEjcn4XW5G6mrZ4Swk4JMIyF9OenZs2PK/i1PedhkON998MzfffPNpDUhFRUVFBWx6G5cXXY6iKMTGxp7ClTeyzr6L/IlFANR2Vh8jPAA0RgMao4Gwst0Ui3o2bdrFWRMmo2/8grlRUGLq59z8cHZsa2TK5GCJeEEUUGTlqNwe3y5NdgcLKmu5tWjkgJg4jMGs57KxybyztY5LixLR6Y7NNurudaP0eRA9ThSfB5y9KD4Nvk3LQR5c0s1Tayfk2mthWf2Q4uAwmZmwcydcemkwURgEhUO0rZOLzvWyfqcFozEoTD75BBobJc746VQcZS+SbWphPTOGnOtQombciCmU+RLoUrRs3yUz8XwDE8enkxobzU0vaHG16VmlGY8QEPDLJ17eTxAr8q3w3Ur2rqKioqIyQExMDFVVVTgcDiwWy7Cvs4aFULL5ALFaG4aEE2e3jPI0kHgoOibIWYR2vcDVZ53FW4s/J0o5kpgqJCmU7qpWwrNORQx9fbxWcZBfFucfIzwOIwgCl4xOZP6Oeq4Ym4wkDW636p+7OSMtwBNLJnPWrXEoCgh60E0/f1A7RVEILF+GKIpU1Wn41zPwpQSpA1x0EbzzDsyeDbmHigpXVoK3fxXoJXydOfT6o1n80j4aK7PRamRWvt/KqIu83HpfHk29/Xh6m3C6tJSs3DbQr9adwJIFoWRFlPLRolS6WyJ4Y2kbby8v4DcPdTFhahSbV/WTENbIC+9HgF/HqDE+SrbpkEQfwfq4Ry/xR2+9KLS1/Xv9PlTxoaKiovIdJiMjg71795Kbm4tmmPklCidmcbCsgY7tjYy4evQJ2wrSsf4VgggaScOFs2bzzier2LLVwtgx8USnx7P/gy2IRg2hiZGnM52vRJxOe1zhcRi9VuKc/HgW7W7iguLB0Ziu8pdIGZfAyIbJdHcHHUSTk4/tw7evBCEtg8sug6p9Ebg5fnSIRgPz5x/57HV7+MWDO6kNJPHbW/wsWRRHTUMoD78wnaQkGF8ELc54LvrdCAyWAOdO1WMJN6N0RlF4RupAPyOmBbdufvZwHrm5EBYLnXojkl5i8uxiWlu+4K3GXEr/lEmg24fWr6F2fwCDzoPTo+f42yvBrRfHydPJfKOo4kNFRUXlO05eXh6lpaUUFBQMOu4PBDjY3UtpZzcOr/eoMwIWvZ6ChGOdL49BGpzqoK/hAMKhKrE2k5EpE0ew5fNSWit7mDUjlbxLx3Nwdem/RXzoJZHOvn4izCEnbLeyrJUzcwf7yvjraznr0ikYL7iOuXec+D6exnbO/N0oWlthWqGXyvbj56o6mtqKTfQ0H2DU2PVs2fQbfvnjBHrtTgRB4ZKZ7fzihmae+KSA+fNFHrnXSUasnSt+msKunbH86ld97NznZPTIGBYvXYvX6+O8C7op3VXC8vU/xuMPoaO7GEXw8cETvycq34Tky0brctLmi6Dfo0HxHP1d/vsjWk6EKj5UVFRUvuNIkkR6ejq3rXiNXuGIL4OAQLheR6LZhPlQhMjh992dnb1UdDk594CfzOQcdPrhLaBbP/gcU2oBLR+vGjgWj58xU1Poruylu7wL2Reg7JPthOfGEZ11bBjqiXwkhkuv10e3y02K1TwQERPhVnhwQxXpYdbgJoJwOIlX0PVWlkEjCvQ2OUj1CmTFCujqtqL4fQjWCIwXXHfS+ypeH59sjMFohMcfh7K1PnqG6aPZV3UAnS1AvvlyBIeFbSUyiqJHDsDGfWFoEiUCzU34SnvISI1m104DVwDbdwjExVsw+vtZvPogXq+PC88LJhN7TZ7L1uoGwn70T6KX5FLbP5rE8ams2p5FdmgNn9VMpbf/8Hd7Ks6kgyNgvm1U8aGioqLyH0BISAg/HzOVfm8/BVEFJ7+AYNn75tYGNpSsZmv3DiRRYrSlMHjycLhrbzm6rU/hCfgx9lno1dnQmwcXBbNmWnlq/lOcmX8mfruPgHsp8fEpbN1dhaX5iP/Hxo1hPPFEFtnZfZjNftasqSCsz0CI6fiWiv1NvdTt2T/w+XBG0C0dXfTIcH5nHVpFYnl1DaaCRPKirPClTO+yokCDSLIlJ9iHwUhpWz/LN5cQqqsnwmqiQAusef+Ez6tF1tBSZ2VLzxiKi2V27hSZkOPms61wnBJmgwiRpxA/NpWpU0W27Qge0+uD0S81DVqeeSsCKQEQuplrWsW7VWcO+Ir09sJjf43lL484eOnV5cjnzEQURUrK+gnPayamS8IS5qW2W2RTvZXyHSbK6hPp7T/RFst3F1V8qKioqPyHkG5LZ03DGjwBD3rp5JmhRVEkIS6ZhLhk1mzczM+K78Ci/7Lj6jwAFu9ezMi8Yny6ciZPLx7U4h8r/sG21CYyLU6uPXM6XV1xlJW9iy3WQ37+REJDk/H74b77gotoR4eByEh48YVofvWDXaTPHDnQ15etIkXbbEwoCPpmdPW04HW009rXQ8e+MpLsIrFjE+jt6WZ6sY3L5g2dviEgB1jq3cDZ04POsYqsIAcUutqjKN9Vh98XIGViEdohImCOpnzzFlpTfPTUdtJT6kLji+PtVyPRhkBW1gkvBSAkI5xJY/zsrdAdyuIKh+ummkxQWBiMWtHmFRCakcMHVyoIusHPJSLewp23XMHe9xYTN6eIsr6P6O8Yza2XTmBDVTqmaBNVidkoyaHsem/odPLDQ+CGG/59ReZOu7CcioqKisq3z+T4yWxr2XbyhkdxoPsAPZ6eIYTHEdr624ixxQx5Ltoczdycc1nRvp+3tnzGgaZSzOYU0tPPo6GhmogI0Gphy5bg1kdYWNDq0niwGW9UFJ5A4KTFzFbsXUtlewOdfi2mddVc0yeR3B1C18ptnP29q4ixxvHp8tf4dPlrvPL2o9Q0lg9cW9fahOI70pcgCkhakaj4cKaeU4Q1yoCn3zfEXQdjdr1Pkft17s9vQO6y0d0NY4oqyE5r4vFHD57wWtkX4N0XXRhsYA7xIUkBREFGQAYUFDnAiPRKvK4e7E2lONor6W6u5sJz7cyc1s/fn3AO9GUIt5BRlEXbH+7hD+4N6CuM/PyqZPbt06PpsRPesY35f4lGkb/bfh0nQrV8qKioqPwHoRE1A8nFPH4PdY466ux1NPQ14PYHE2Id9pGQFRlFUUi0JPKHKX84bp9vHthEbSC4fbKpz03dnjKihR6i/RXo9VbKO8uZFDqH0pgo1pVtJrTAwLzCX+J3dXD3j1vQ6Xzo9VpCQmS6uwX27Pejb/CSPkLPO40NpG9u5MWn86muM7N5czBy5PCbflFxFb99WMNfngwnP2Uk/YtWQFQYITddQMszHzF6VNAyM21SMBxWlmXmf/o0qQk5A+NPi0tif0X1cecnaST8fv9xzx9mVNGdHNy7l35Fx0vPO4hMNtG8u464UQVU7NxCb1c7GSPHH3Odq89LyfN76DIW0GPehN83kkAgLCgOhCNbIrt2WBBkEUHWoggKi5aYyUr38bv7m3n+OZGN2zR4PWHo9KEIHrD2WdBlpfBW+rPIDieRj77O/tK13HHxGERBRhR8yMrQ2WCHw78z14cqPlRUVFT+w1jbsJbSzlIkQSLOHEeyOZlxseOw6q0nv3gIPquvZI8vgzm1DcQnJXFNQS6flrxMYeH17Nv3LveeeS9vbf2Q7h4XRRHFdNdsYGfzo5x3zV109J5FQAZQ8AdkBFEkKqEHs9ZAvF5h11/S+KhGh+D3UTTKQ0enSL9Tob7DR10nXKI1EhcWS35KUPyEnDsL1+LFKF4fyUnQ4yyncsGW/2/vvuPjqs7E/3/unT6SRr3LlmxLLnLvNu422JhqCIGEACGQLEkw2SSbbEL4JYFN+JLN7qbsJgGSUEIIYEIxvdi4Yxv33iQXdVm9a+o9vz/Gli1bskayqnner5deSHfOPfc5M8L30bmnYLJY0DSdAxV5zL3u4kUsz87QaYvVZsXr6bjnIypqMBNnB+feHlq7i7jB53qChk+cRmneCfZvXoOm6RgBA6vTROa4aeSsPkxdcT7V2wqoc44lIraBuobgIBGL1cDvNRMwTBw6lYglDCLSgp9TURXMnA8RybEsWgZ7jxtUHvoILApKXJhuvYtAVTF6YySN+zYQqxSjsudwrKKMgN1LeHQNdQVtzBUeACT5EEKIAWZM3BhuHHZjxwVDlBYRxn8MHcGKj9aSnZLEwUNH0coDnD7wIacONlFeuo70jz/hu8V5lH39Tq4a9SW+c08tTkcl4T4ztQ120MAwdJSh4WuyEZZexalcRfbwU5xkHKdrzfjzLdTVKPIL4JPNYAtYeDYilagLZgRbRmTi/mQ1enOAQTfc1XK8tuYUccc/JClu0MWNuMSYS5OuU5RXhtvtoaaqjuJ9uQzOPDdLp7zkNIkZKWhomHSNwtwCxlw14aJ6ktKHkpQ+tOXnproGjuzYwd+LTjK9YQc3/OutfPDdJrymMJzOBjzuMBLizAQCEBYGf/wjLY+fnn/+4tVRNV0neeJSjnz2TzYeOkQEUYwYmo7HGk1Boo+t3/s9eTEHSIpaTlnxCHw1MQRH3w68ERSSfAghxAATaWtnuc0uGhoRQZ6vgmmeJkZnDIJAgNjCeCKTp1PW9E9ySnaxMGYkvlEuTCdq2LfmN+yq+CHl9dHgN6FrBoYycXbqpk13MXdpEe/+I52wKI30tCoGD4f8HCfK38Tg5EawK8aPTSA2vYHKvNbxmIdmYR568QjPVdteYmjazew4eIBAIEDAMPD7/RiGoqm5/T1YLDhpqq4nMs5HTGIENlM6I84bBKt8AQxvAANFQBmkTxiOxRZ8nOEzatqt1+kKZ8yMOQyObKbKNooliybx9Rsf4YH/7ycUHt7N8kfScXtgeKYX5czkwqmtba2OWnBsI2abk298/we89spKcpvriYhLICcrFvuIespNMSTbt7LvkbE0u0ObPt0fSfIhhBADzNmxHKHseHuW3whQ2FxHU8Agt76S5UdKMftLsFjjmWav5h/Hqrm/1I9zyz40IKwikkirTlOyG5d7MO8NdpFtuooplZ8wZKiLpEAYJd4wbptWyMe56dTUaowZY3Akx09Flc7+z9Jwek9zZG+Am676jD99cA1lpeC0a+j2eGZOPEKDP4FRY+vZlNdh+ACMcI8nLNKF+czut2bdhNlswmwy47jEOib2MCvJg5IYOiK4I2/5VAsnV+YyZFkmAJrFhMliwgRcOILCokd1GNdDoxZTcPyPRES6+MEf/w+AUcnJfLIFAh4PlZ98xN0/qmbRgklkjzl3271wddTyov2czstnzFVfAeDW229i08sv4NTCGT5qEIXOdK522kkeXs0jb7zOljWHefndG8ktSKfKE8Jc4H5Ekg8hhBhgJiZMZFvpNqYnt7PmN1DQUMa26kqqfW7cfg+FzY1oKGbFxoNS/N/wWCbHjiPNGQXA6/tOEnOwhBl3zGTl9j8SllKCUbUPV0E+JyrDsYwdRjg/J87k5z3vMhwTLKhiMy9tSUfXFG6fYvceH/GRzcyYbOK3D1Thzs/nK0+M44+vXc+U0WtZ3zyLCIvO9x5SPPmnGLKy4ejB9mfgnN71HyRO+lnLzy6HjfTUNh65dMBsNVGdX0mZRQe/geYzCDR2PAC1M4x9xykveIoLF+/SzGY+KpzOuJlJPDT3cT5oeIQtWy++9dZVFVJwZDOTFj3QckzXdeZ+5V5Wb9/Ovq1bmZJk4dovzuHa8am89Y9CYqdX419noi4QRfDxi0ZnFg6bPburrb18knwIIcQAE2mLxFAG1e5qou3RAByrLWH16VP4VHAuTILdxYzoOBLs4TgtTgylqKh1U+kJ0OQ3cAcMdK+Vvx8q4KbRSdw4YjD/OFzFkVcfoNQWIMmVTt7xMJLzFJOSKlGnTxNWuZSXHYOoH3uMpnH5zGyu5FRJAtW5cZisiuuvbyI86UOqd8ym0uolIzGNNe/C134axumCGczMyGXDkWwef7yU2oZ4Dp3yMGtOBRH+AJX7V2H4GtDNYXiaCrA5B9Ncf4zKfb/j7A3VYw+O0+jsCqoOi05UAOxWE4RbwKyTPDTER1ch9i6Zxw4l7oYH2uyNKvoVzFgCyUv/lfTfP8tm9Q0uTBIObXmVaUu/22bdV0+dytVTp7Lx7Xyum57Pk79QpBqDGTHxQZ6Y/Xvuzv05ZQ2xF9V5Ka6ujU3uNpJ8CCHEADQjeQZ/OLoWpQUfNwxyRnPfsKnYTW3/s65r8Nr+00xMdhFntxBnsfCHLXmMiQ/nqc/ymGTXuU7lk3Dbk1Tv/CX7Xq/FEZdNgWscGbY4mm1DONVkZ45vFdurhrFwto31n2bS0GwQH1WOCqvj7y8M5onnG8jd6+NUVR6FuR6uqvbzxAM5mMrqiBiczdGcPKrHRXPjlODAzZMn9zBkyINA9kUxx573vc/joeLIui69VyaLjjXRgSszqkvnh0LT9XYfg50bWBrFqbAv0pR/EBjTqozTFdvhpnnxwwcTllxPldrPlLuvAuBN5zeJTtAoa+hcvLfc0ncLjIEkH0IIMSBpmsaMhKl8eqISp9lEUYPBX8qKUK1mfbTeRt1r09hRUM2/LgwO5vx/KaMA2H+6jlMfPkdZlRfP2rX4AuOpCtvHqOhTDC/MxDHUxacnjzGf51mdMAhbYS1j177BgqVjmZTRxOu5c9h1wsW/vbqcbZ99EXvAzmd7i3ji0eBeKtqnq6mpep/4r3wJX1I+pza8AVMWdaq9FWVFfP/H0/H4grvRdoZuMaH8RscF26JCXbpc4aupxBJ1LmU620Pz17+eP7A0GlucRvF7fyLl+m93WKvfH9zdtqYm2O6wqAgiYqI4umU/I2aOZfPGIgLNFsxmF35/19f86G2SfAghxAA1MTKME1oN12Un4TCZMJs6nnL5j/ePEfAbmMzBskeOwP/8ysX/fnc89YWlWCxeIlxOEt172Fo4Ak+Tn/HmOfhP1fPo6ARiMyK5tsJHSnEiOf5kimJ81I/NIWfVVRxa95/MH2dDHxbFrx77ass1Y2Zfg17RRPlTT2NuaiLRdwxPYxm2sISQ2/r6a41kj07h/q/DfffB8OGhv0+aWcfwd3H/kxAfuyRe+3WKXvlP0m7/Mbql9dzhCweWQhR1OZM5/ckLJC4KJmiqnSRn5cpgW//f/4O//CW4PHvm1GzWvvQcbn8pc6amkFs+BHtNgAa/iY6n3faPfWAk+RBCiAHKbNLJig+nttFHRHRof/VaYuw8ufIQ374lG/28ZCVizFU0l7+N1RWG/fgbNPq85JnDiB2eSWngE0Zke5lg2Gk4XoerppKmzOEccsVyyBZOs9ngF8/obPpoAzMjI4lNTQAmtLpu1LKbz31fU0nVnveprz3A/hg7+7RPO4x77bEI0oc28v7BGjyWSE5VGqzYVQ2A1aRzy/iLd9c9S9O7vgz50W37KaspO1fXJZKRMqMO34r/4UcrH27pqWiPK2s6/sZaKra8QdzMW9stl5sLkycHv582LZh8AFx1yy0c3PoqX15g5uk3E2hojufSYz7611LsknwIIcQANmlwNB8dLCUt2tlhWaUUxyoa+NrcoUyb04AZO1MnB/9K18xWdLsTLSyW4zYb4bMXs8Rlxc0LRMUMwpodjm93MZkRczAmJRGTMYd9O8sImJpxOq08sT+fV++8kYChWP3Irzm55lPCh6QDoFvMzLjt2pY4rFGxJM26m/jmRko/fpk5M2ZdMm6/H57YDbs+hBtuALsfMmLhjknB3XJX7Crs6tvXoZTkGQyfOy+kstUjc/jzbw4T7dnDL+7dxz/XZlF4PJ7DL25u9xxP02mK/X+jsaLtHokLFyI7y+aIwmy2QZ3OIJeHk+YAyh/qLV3r0/EeIMmHEEIMeGFWM16/gdV86S73V7YXcOukNLZscLJ4PtxwWwnPPq9zstjBqx+VMLH+OL5DGylMXUe0NYk4eyaRSd+mtLmZ2tpjlJENRyuoyvczb3wCN5XvIr95J77EGGJLMjhZm0FWoovrf/vzVtfd9dYqSk8UkjQ0rdVxkyMMzdLx8uArV8L8+cFegNWrg8MwQtlltluEPOYDouOzUJFZXHsvjPrCBG4eCyd+C6PuyrzkeQGfj21vv97ma20tRHZWcY7GVx6bxKGCMJTqXz0bHZHkQwghBrixaZHsK6xhSkb7C00ZhsGpyka+PG0wb5/pyr9qUjJhJviPn5YyQttG8uK7cFjMpJfegTMpE8+GVzmx5zVywlIIb0hgT3kzN/70/6M2v5ir//4q79xyHY539rDd+IxG8x5e21vAksyrqW+oI6/SS5OWhM1iIn7IaE785/9y29O/CrlNW4o20xhQ+BS8sz2VpAw3L/5yCEcP2vjtb3tvpkZnR0i011NxKZ+++iJTb7qtzdcuHi8SZBgG2w9N4lixvTP5EQAR7S+t0msk+RBCiAEu0mGhqtHb7usNbj+/+uAwDy0KdhecvUHesizAiidX4WsYTLynnPrN/6DBbMdfX4O/8iXCb/ke2UvuIxv45K9PcNy2nWef/gf60EHclRrPj55+nanO6dx8aj+3TBqBqviIhrQ0Cg6/iNs2nKWzHiLcEUNxTTNbxk5pM7YL/14vb8jnnye3MSoum+mJI7BoGvVTND7eXElJUwU7dqRe8r1QhkL5DAyPn4A3QMATwNPgQwW6NtulM6vIwqV7Ktqyb/UHZIyfjKOTGcGJT3ezbk0CPr+OCnk8RzBLubX9ISa9RpIPIYS4AqTHhpFb1kBmQnir4wXVTfx5/Ql+cv0onNbgP/lnb5ALFgZIiR2BK9KBLWsusSODCYLh93PyH38nMja5pZ40Zzr/cttM/vLJ88zKv4vjeiy/+u48Xnx9OyXp32SbdxcRjsVENO4n6oiDKcMcnPhgM3ZrcB2SqLxtfPZmReugNSg04A+HPyZgBIizhVHlbaLZU0q84SLCHFz749Zb4NkXrNyw0M7QYQ243Tof7qsBFbzt2nJqyK0IoIzgkvOaRUMz65isOrrVjMmqkzar/QGpF+rsImbna6+noi2FRw4RCAQYPGZcp6/TeHgbjZavoULaVO5ccpKc0noF1r4iyYcQQlwBRiRF8NHBUlKjHDisJgB251fz8cHTPHZTdqsFrM7eIOtq6inJPU6C1ozZdW6Rr6aSEkzOsNYX0HXmZM3n4IqVWMM/4L4hi/n78y+ys8pM4kIXEW4ni8fdhF23cGLwPxgz906sg88lLyZ7IdOWPcCFzi4QX9JUQ6m7nokxg1BK8fq6h4mt9JFzwskfn83k6Z+tJWHoEhyxZ7e5PzfAtq4ygGvhwNpa3t3YQO72Lcy/+/5On+v3w9f/uIA9hzqfRPh9fZ94gCQfQghxxbh6VCLv7C1m6dgk1h8t52RFIz9aeq7vXylFfUM91acLaCgvwFtfQWRsMo2H15OYPR8Af3MTBf9cwYjvfr/lvIayMgJNzQAM1b1c/a3/Qt/0P5RbS/BFLqHO56Sh1sfLq59naNxQrorKpuaN1UTfdSOWuKiQYk92RpF8Zp8Zn+EjOXwxyeMWUmsFRwykT72nW96j9ly4mFdPUkqx+dWXmPuVe7t0/j9frCci+igqMKzT53o8knwIIYToRiZd44ZxyTzzyX7Cdv2FhdGN5BwFpZsxTDYUOhZ/E3pYHJHh8ZitdkzNJiKWfI+m/GIaiwqo2b6d4d/5LnV5eZzevAUAS1Qkw79yJwCjr5qGbgujPncLs0u2UBh/NfW10TTqZiwVFVhqEykaPITAhueIuHlhS/KhOhi6uS1nGx/nfExWXBYBv8b/fn8eSnlIT/dhsznpePGs1jr76KStxbx6yra3XmPc1UswW60dFz6jvrqQytJ9JAxezDcesBDgOgKq87fwvl5W/SxJPoQQ4gpiNuk8sHg8BcZSat54iyafD91ioFt96FYzbj0S5WtG+U+g/H4wDDCtR4uKxhQdjSkmniMvrcCeksyg629CN5kAhd8bwO9tIuD1Y9RW48yYzp7AEJqLc4k1x6JrMYxvWEyYVkF+7VYiE2OxpIW+gum0rGmcrDnJHVPv4LXXwJFcxIqnfPzhD3727fcC9m55fxobvLz5YQ5eX4Bwh4XoCCtoGi+95uD2G+MAS8tiXvW1bqryijm1btu5GSVnvmlZkVSpYGKlzp+Ve+ZnzpUJfqsoL86jrqaCSksClftOnQvsTIfEufGt2pnBrhpoUFGzhUGTp/H1rx6m0Tuaro7b6A+JB0jyIYQQV6RB115P6uKlHH1zB/6AByLA19SEyWLGZLVhstrQrVZMpnMro2rndS4ooPho/kX1msLG4T+6C1zTcMZX4NH2kB/2AUtKpmN2ejheU0PNNIXZHM/Rf/8hpWmjGW1xY88/xKsFK/jTi9MIC/Pys4fWolssTL3h3JgH7cwNNTcXJkzwkZAYzty5DezZF9pc0lAenWgaZCRHEB1jZ1h6NHZnsP1F+R6e/2cVscmNfPB+BMUldv72Vi7DY0YzYeLQM+e2zhA0/WzMWvCY1jp5CP58LoFAg2HMQrtwA7nzmqfU2Szm/KRGEX6kmfi0yaxaH07nJwD3jyXVzyfJhxBCXKF0XWfUF6YRqPfiLagHk4Yt3YVu755/+ocrReCJ00Qe+gfHnQcx2zOxBOIo3X+c1Okz2Vtewl7jDcaFDcYbmcW4a+4geg1ERsL0W0awbeXTbdabmQkfv2hiy+Z83nnXijJC2/+9o0cnf1t5iKQIG7GRdkaPat0rc9ddNt59N5FfPR6cIpuSDMvvmci2NSexR7aeQdSTtHZ6NHQdMJmpqQmW6lxCcW5zwf5Ckg8hhLjCmSKsOLJjUQGFN78OwxMAQDNpmFxWdEfwVmA0+wnUeVEBhaaBfUT7i5ZB8K/6KH8ide75eDMsfBxjobK8mtO1c0k4cJBnR2SSosVyz+++zJbcaTj/0ESY08+secFkwl1d2Wa9y5bBn34Xxo9/EsXIkRAbG9ojhvb2QTkrxWXnmoVD2zy3M1Nku0tnxqXo+cc4te0JAv6PAVMXrqZC3SOvV0jyIYQQnxOaScM2JLLlZxUwCNT7CDT4QNPQHSZsQyPRTDru3BoMbwDdeukbXfh1i1hxPIOihsPUWNeyO+lepoZvZcysuQze/Bv8J76EL3YQ987ykDXEx7PP+YBg8mGLap3cnB1HYTbDH36ZR/b8Syc/59qhcB+vYbDDxLaPTSzNauTTN2z4Kyw0fFaD8huogKLuZC3b1pzsxBumEShtDL18D6rwL+Sd3ACqS4lH/yPJhxBCfE5pJh1zlA2ibBe9Zkt34cmrxZ4Zfck6nGPiGbn7EyILTxGu38TwKfWMajrOlk/zsccM51hJIpYIK3MWRpOZCX9/s5iivBI+fP0IW4tcFGx+DYseHHcRqA72yGx+fQ3ZMyeE3I6w6Un4Tjdx01KD1z6yc/23oxmRpdAjNOyjYtDNGph1bpmV0mon31DUrbl43Mvl6MyU3iNH4OGHg9/nHkvn4KEHu3DFc90dpn6Ut0jyIYQQ4iKaRUf5Ox4jYLZbOe534QwfxJjhmUy3NRAxKoN83xh8Dgvj5l7FM8/W8NqzueQWxVBcb2fW5HCmLZjO6R1Hue2qiS11rVu3jn0fb2PE1NFEpYTW6wFgdtkwu4IJ1BvvX/RqyPX0hs5O6a2vh717obLC0Q0jNvrPc5f+9akIIYToP0K8V5n3Hydm/hrUAYPSpACnbUeIbYwl0u7AaX2ZKP1uPtwUhdtrwmI2cHs8HD9ejaOsoVU9uq6jlMLmdPRAY/qHjsal+P1w++2waRPU1oK3/S17BjRJPoQQQrQp1L+Txy/Zzb4T38Skohlht7I7M5G6fRqfJYWz/tejsXjN3LrUQ5XHSWSkzvPPO4BUwhrLW9VjBAwa6hspPVFEZlxoM1x6ilIKDDq9Y2xHOtr1duXKM0uvmM5PPC43iP4zy+UsST6EEEJclkFNkSTpJ2gocXHI4SHX7iGQe4zyN2cS5alAGRbWbYpFsweYODFAYakH3aJT7/ZQWFuPWdcw6Tp5x06xaM7VxA6Kp7mqEcPjxggECPj8GIEAht+P4Q+gDAPD5ycQMFCB4DGjwQcKNE3vvocLGjQV1RBJ9+0b09Gut0ePnuv1COq+xCE9vduqumySfAghhGiTKdKGv8aNOerSq4vu51/4zNREeszbXO3fTeG25awdk0b1vuG44k7gNLv40qQNPLfniwSAJrcf1WBgS00h5/RpAobCUIrE7PGU+DWKC2s4cPwESxMi0UwmdJOObjKjW0yYbVZ0kwndbGo5ppvMmMKtmGzdf0s79cG2bq2vvSm9J9dsRyutpnTfBDTicTo16uoMunOcxuzZ3VbVZZPkQwghRJvMCU48OdUdJh9TRvtJ3fIS/0w9xVu6h6EVu4nNCVAYN5iXP53BrTfVURhzPWllZlxhMDzj7J4mse3WeaKhkOSp2e2+fqXR3AaWsGbU8W1YA7OpqIukOxOPYcO0frO0OnR2px4hhBCfG5quhdTpHzdqLCNHXsMU8zgeuG81eyMH4YtPxzX5IM0WC//zYgpHcyD/tIcqbyMlTXU9Hnt3Ut098KMNGhr+zEk0xtXT2G3b3p+rp6ysm6rsJpJ8CCGEaFcot0HNYqWu7CgNhpd/bNvJF+5fyq5xE8gZCb/74z8Ym3mYEwerGDSqiWa/n6dP7O7xuLuNBspv9PhlLBGKuvp3iBxsoa7p/OXc+8/02O7UqeTjiSeeYOrUqURERJCQkMCyZcs4evToReUOHz7MTTfdRGRkJBEREcyYMYP8/O5dqEUIIUTPC/Vvfv+Y28kYeyd3zZjC8OREfjdvIlNTh+B1FPHkoy+y67ME3nk+mm/8Vz63pAzr0Zi7k241E/D6evw6J8pzyLEVs6tg9AVTbK68mS7QyeRj/fr1PPjgg2zdupVVq1bh9/tZvHgxjY3nlp89fvw4s2fPZuTIkaxbt469e/fy05/+FLu9e7ZDFkII0XtMYRYCdZ4OyyWNSsdZ42l1U5lcYXBofz1/LTnFf76zCmUYWHQNtH601GYHzHYrvkZ3j1/HkZRBkvcoOSc1TGYz3Z00JCR0XKY3dWrA6Ycfftjq5+eee46EhAR27tzJ3LlzAXjkkUe47rrr+PWvf91SbujQtjfyEUII0b9Z0yJoPlCBY8zFS7BfaMT00Wz5yyu4EsMA8LqbKamxM98yl08L8/mR5xUmj83iqdN7yAzL5TvDZ2IxtX0b6i9/r5sdVnzNzUBUj16naFcOzdZs/HnR+P3dO8ulP7qs2S61ZyYix8QEl8E1DIP33nuPf//3f2fJkiXs3r2bIUOG8PDDD7Ns2bI26/B4PHg857LqurqBNRBJCCGudOZ4B76yJiwJzkuWc8ZEMDI8EXtqCmFTgxuXOF4djOZzoxq8mJRG7ck6bFYzhz0BfMrA0k5d/eXWa3bY8TV13PNzuTwuWLshk4xhxTQVhdNUc+n3eqDTVBeH8SqluPnmm6murmbjxo0AlJaWkpycjNPp5Je//CULFizgww8/5Cc/+Qlr165l3rx5F9Xz6KOP8thjj110vLa2Fperb1e4E0IIERTs/YjrsJzh8VL065XsygpuSDdhQibpI4cEXzMM1v3f67hHjiensRG98jjWcAeuzCQ0wO/zYLYEe1gibDauHzemx9oTqurcIjwHq4hIT0TTtWBWpAX/G/xZCw5g0DU0NEyxdizRnR9msP+TP/OHD8aRfPBjVpy4jZzckQQMDTC4/LkhGiYT3HUXPTrdtq6ujsjIyJDu313u+Vi+fDn79u1j06ZNLccMIzgi+Oabb+Z73/seABMmTGDz5s089dRTbSYfDz/8MN///vdbBT9o0KCuhiWEEKIHWNLC8RbWY02LuGQ5X0k5R1UVKxojiLGZmOqrAoLJh2pyMzE1ieglw8+UnkhDUQXlu3NBKQ5XPcaMGX8hZkT3rSh62TTQYixYksJAKZRBcEBoQAV/VgqMM0uxGwYNGwuJmJfW6ctEWdOZMX40ays0nnv8BI9922DVtpFY7W6am8MuuxlGz0/Y6ZQuJR8PPfQQb7/9Nhs2bCAt7dybHBcXh9lsJju79cIwo0aNapWknM9ms2GzdfwsUQghRN8xR9lpLm3qsFzz/kNMmz4B9fozxMUYeMLSKazcit/XiKNoKPF33NiqfHhqHOGpcSilaHx3Kt7GZo6/+Skx49KJHtb5m3h305RCj7JgTQoxAQgYuI9Ud/o6n+wpIHLSBj74t6t58w0zifHNmM0BTLoPR0Q97oZwlOrMw6izZYMPN3phqZJO6VTyoZTioYce4s0332TdunUMGTKk1etWq5WpU6deNP322LFjpPenReWFEEJ0Xgj3vqgbr+HYm+9zeGQ2KZU+Rvt2ExE9GcOv4T/oR3e0/cempmmMufGXABgTsijfncvJ97aCBmablZjxQwiLi+7O1oROC/2m78ju+NHUhU6uL2Rc9jW8uNLMl+/U+N1vNG5ZXot/bTizk8sZnbWbJ9+cS0l1LKAI+C2ofjMqpms6lXw8+OCDvPTSS7z11ltERERQWloKQGRkJA5HcAvkH/7wh9xxxx3MnTu3ZczHO++8w7p167o9eCGEEL3HFG7FX+PBHHXp3uqsZUtZsXEo7roVTDQ0YjIfxCjMpdaWj9HkQ3e2N8w0SNd1EicPb/nZ29BE+Z7j5J06yFFbMyaHjs1iwmyxMGxEFhmpPTePVHmM4COWHnJ8TT6RKeEMGRnDR9sMGt3l/OnpQsYPiWbV3xJ49Xg6amMGmtIxlEa4owlzvIfaUicBFfqU5U7kT72iU6NYnnzySWpra5k/fz7JycktXytWrGgpc8stt/DUU0/x61//mrFjx/LXv/6V119/ndn9aUcbIYQQnWZNDcdXWN9hOU3TWD55CHea7yY142f49m6l/oAXLS4TrQubv1nDnaTMGkN1dCK3fPEarlu6gNnzZzFh8gSK8vL523OvcOxEYVea1DGLDpaeuXPnbS3GlegkbmRwxmhWlg6BRAyfwdtvONDODNQIGDoBQ8MwNHxKY/k3/gOdQKeuNeAfu4Tivvvu47777utSQEIIIfovc6ITX2ljcADmJUSH2YhcnEHtWyeImD8Ts1FJxOzULl93y0fHyZycFIzBpGM26YTZLMyYNpH1H64m/pbrulz3pWjQY4uO6AaEJ59bSn3ZMnjlFfjonTG4vRomq8LdZMJucaMMDbfPidfj4L+e/iWBdicpt21A93wIIYT4fLPEO/FXNKOMju/Iuq7jmBBP7QcnCZuW2OVr5hwux2Izk5h08Uwbk9nE8h9+h63b93W5/ktRPbjcmdlqwu8514NhNsNrr8H37t7K7HEHidTrMKPwKSv+M1mQxeJleOIRjE4NPoWhQ3t2mm1nSfIhhBCiU2zDo3EfrgyprH1IJLF3jkK3XsaalrqGr9nH1lXH23zZYTPhDyEZ6iothMGdR47Avfd2rl6TVSfg9V90vMF5mrRFJTSZwvD6Lfi9FvxeB6DhpJFjOVmduxCQm9vpU3rUZa1wKoQQ4vNHt5qwJIfTvPMIpjDODCg482Wc9/3ZR/XKOPN1YTnOK6cuKHOmXqVIR5Eeq/j0YCkHtuaeW19DGShlEPD7CcvdQal+pPU1zlSjaRpo6rxqg3NFzp8xop3Xw3H2uIbCU+UhfMzS7n4Lg/WbdQzfxUnTkCSoKjiCZlpAQowXw9CoagDDb6HKHdsjsfQ2ST6EEEJ0mjnGjl5eDA0VqPjRBFfj0gD9zM1eO++YBrp+7uezAxDOL6OdOx/tTFm9dR0zR6szr2vomo6mn/mvpqHNWoqm6RddN5iGKJSh0HQN7Uz5UPlPnUC5295Yzu+HL30Jampg1KjOv4euwRHkbSyi6kQt9lMf4DDnsud0Ir/5xwKO1F9HZZkT3RTAhIYFg55f5L33SPIhhBCiS/QRC+HoB5CUAdbLX4WzI13ZG/1MKtLlQQYKRWW1D1NJPf6AgdtvtHSuvP++mfh4M//5qyZeWWGjuMjMyZxG4qyW1muitAShnakTUAGUqZbk8bE4bODNvJZ9K1bwgz9czYmqYfgswZ1tjYCJ4JyXrjVA1yGs5z+aTpPkQwghRNdlLYZDb8GYW/s6kh5RhsGWKg9DnA2YNA2H1Xx2axdOnbIyOtuHMhTjxvrZudPMxs+KuWZs0rkKFMHCZx9Bndmw1uNupLx0C4Wf2XFGRPDwM2M5XPQd3N4zKVbbnS0d0jQFSiM5uZExo6o5dDSWgkJH19+AHiLJhxBCiK7TTZA+C468DyOW9r85nZdJ0zXGD3OQNTL5otdmzIA9eyBzOKzfCC4XRMc7SR4f32G9RiBAw7ZTZGb/mY+33IBfVWC2ODD7LZjMXjzeUBOG4OgVALtdYbNBSkwDI4Y1Mm9ePbmnUkJvbC+S2S5CCCEuT0QiDJoOB9+Apqq+jqZbXWp5q2XLgrNcFi2CXbuCxzRNa9lk9VJ0kwnQ2J00io2+VHxhbtwGmMw+fL7O7HcWDNDlgjtuh/u/pohODuOF/1Ps2D80OA6mH5KeDyGEEJcvLBZG3wp5n4K7DmIzIS6r/Z6QgB/qSyCq/+9i3t5U27Prcpzv3Y8hYJwZX3sJXq+PnJxdNJhiKXF4iXPXUWx3U1cdQUib6Jy3cdyguBr2/Pk9PAE/Hr+Nl164mUGTo0gbYg2hnr4hyYcQQojuoWmQcWYrjcrjkLPqzMwVLtxkNfhzeCIcPQC6BQbPAFs4/Y3b5+ODo0cZ1lRBcPaMdm4i7tmFvjRQSsNvKBqOKEp187nFyS7oOVEq+DY11DQQZZ1ITfVLfG/sUf531b/hc3c28QC73c+8axw0D0lGN1kIt4exb8tBiisbGT9z/uU2v8dI8iGEEKL7xQ4LfnUkaSwEfHBiPUSmQcLIno+tE1Ii6rl7QjhhqVedm7gbnK4CKHRdO3Ms+PPh2pcIeKxt5hBnp/gqQDN7eNluMLZpLpFRYdzzxWf5ViKc2Hw9T3+SSklZMu0nIsGMxqQrYmPrqaj2crzu7HLrjQD4/R0/+ulLknwIIYToWyYLZF0dHLTaz5IPAItuxmIO7RFGo1HK2Ov/pcNyPq+HaTsOERkwUVt3mJicYzTow8hP3UdTU/YlzjzXhbTqlWc5WHonO3bGMHduUqtSuf1tSdMLSPIhhBCifwhPhOpTEJ3R15G01okZPBZrIlvfWoveweOTUwe9OGPiGOWI4ZjmYOPsCWQdPsEPbp3MG8+eWWStTcFeD5s1QG39CebMdbJjZ+sSoQx47WuSfAghhOgf0ibDsY/AZAVXf5ki2rk9Y8KiBpM1Zw4m06Vvr9NuhkaPj0N/OciU4Qs4biniZPE4Hrz6GsDUzlnnEhJnmJm0cZPYsaONUpoW8i70fUWSDyGEEP3H8CVwciNU5kLGnH6xbkhnIgje+Nvveaj3+nhzXyk2XeNkg4f07EimLkzne/50hv3YCOlqFgt4PAbLly9g8uS2Y+jMEvJ9QZIPIYQQ/cuQOdBcDcc+BGt48Oc+07keBE0zodp57LFzywG2lDVSm+BgfMQBpg6KpmyHmZioBKprnYSaeAweDP/857uMHr0IqxUCgQC5uScZMmQIJpMJr9eL3tFc3z4myYcQQoj+xxEdXDG18jgU7oC0KX0ShmpZH70zJ7WdsDTWNbL85ukA7N9/kLFDlnLDjw5Q3xjaYNYvfvEEzc0mHntsF01N9VjP7Kezc+dOHA4HmzZtQilFcXExd9xxR+di7mWSfAghhOi/YodB/mdQfhTiR/RREKEnH7qmtzvgM65uF/nvridhztfIyytGqZc5uHMpJrNOwDBQRlu9FRp2O6SmwquvDj1zLL3lVZ/PR01NDdOmTWs5dvr0aUpLS0lNTQ057t7Wv/tlhBBCiMHTobawb5Zu7+y4TU0/t8DYBRLsGs6MafibatlbGMXpLXXYbeUov++S17FaYfbstl/z+XyEXbBtbWJiIvX19f160KkkH0IIIfq/oQugcHtfR9EhTdNQRqDN1wLORGhsouDt9zgRHkt1YCMPjP4f/AGFUm33eug6xMfD88+3fb1du3Yxbty4i47rut6vB53KYxchhBD9n66DMs6tT95LAoYbTXlDLh+fPJKTRza0uvE3N1Yxac49YASI0Cy4Zs9g7KotfGKexPwFR5i97RhbC4fjNewA6LqGyQR33tl+0gHB9TzcbjcRERGtjnu9Xvx+f6fa2dsk+RBCCDEwDJ4BJ9fD0Pm9dkmTbgfj4mQnEPBRWPgchuHj7DMTRYDUlDvJnnxjq7KHd70bPKehhoaKPCLGXsXdKcNoPL6Hb/3zm9TrNq6/ahebj00kNc3LjBlRNDZeOvEA2L59OxMnTmz5WSlFTk4OmqYxcmT/Wyn2fJJ8CCGEGBgc0WB2BGfAhLJvTHfQ4MIBGYZhkJv7/8jIeBCbLa7luN/vprDwbyjlDW5Ap+mkpnwZiy2MQzveJfuWr1P27GN4j+zA0ZjH2qFPMGTkQZ7+2lpeen8krmFOnn/eGVJYSinKy8uZPn16y7H8/HzS0tJwOkOroy9J8iGEEGLgGDwdTm0CXzMkjenxy2lorQaQKhVMPNLTH2iVeACYzXYyMh5o+dnvr6eo6GV0RxNa4T5Or3Jjjw3DEpWI7baHyP0VTBhdT+Kcr7Ikwcmh34YW06ZNm6ivr2fGjBnnxaVoamoaEIkHSPIhhBBioMmYHVyGvReSD87sZXvWiRO/JW3QvdjtSe2ecZbZHEF6enCTuYb8hwmff1ur1zMzYcfWCVQc+IQde29sq4qLGIbBiRMnuOeee1qOKaU4fPgwGRkZIdXRH8hsFyGEEAOPLQLctT1+Gasrk+q81wHw+xvQdQdOR1qn6zFcCRcdW7YMck9F8MV/mcyuXR3X4ff5ee2VlYzKOpd0GYbBsaM5ZGRkDJheD5CeDyGEEAOR3w1me49fxuSIxeEaDoDZHI4/UE9t7V4iI8d3sqaL19wwm+G116Bo3Uekzv9am2cZhsHB3bnUVNaiazpLrl3M0QMn+PSTYLaiAcptZcTIgZN4gCQfQgghBiK/F8y2Xr9sVuaPOHHif7uQfLRPN1vxeZqx2Bwtx/KPF5N3vBgNyBo9hLGTh7e8Nm3uuXU9Sk/UYg+3dFssvUWSDyGEEAOLpwGsffeXvqa1t+X9pQRHOZzbdO7Mf5VB1PAZVBxYS/Lk6wBobnZzZN9xFt9y6Q31yvPr0U0aUQkDq9cDJPkQQggx0BR8BkPm9nUUnWJNnkrdrt8Ffzi7AJl2dtilhuXgR3Am+Ti+5u8MdQ69qI7zNVR78Hn8pGRF90zAPUySDyGEEAOLMsA0sB412JNnYU+e1e7r+1U4MYZB3tZ3sIW5sPra35dFKUXJ8RqypiT2RKi9QpIPIYQQA0zv7lmi/M0EPLV4AtUUF79CVPRV3VOvUnirD1N//FUaKtew8816xl39JawRseR/sqbd807uqWDw6NhuiaGvSPIhhBBigOnd3Vo1ezTHT/4Wu3MwQ4f+EF3v/JiP5oo9GE2nMWk2asrWEeYaSVnhG8QmLyV28s+I0x8NqZ7i3BoSMlzYHAP79j2woxdCCPH508sbzBmGj4z0+7GGDwqpvDL8eCr3Ule8Bn9TMfbosRSeeBKbORpNt5C14G1O7/oZhq+OqJEXT7GtPrgfT4PnouONtR4Mv0F4dO/P8uluknwIIYQYWNKmQsG24FLrvcBscVGb83cih9xO0f5fEhE1gbix372onDIMGvLfo/z48zT4CoiPnkd59Qay0pcxdslmlOFDtwRnpiRNfZyza6T6c3agxaZjiokHoOJUBWGpg2iubcYReW76bUluLZmTL16sbCCS5EMIIcTA4oyBxrJe6/2IHn4PyjCoPPA7XNGTqSh+F0/9cTSTA7+7lKrGfYyY+CQ1J1/FbzTT4CtkUPq/ED3q6ySfV4/WziDZmjdWYEuOx5I1HvvMJWRdvwBP3iFqDmzldL0XFPgdycRlZ/Z4W3uLJB9CCCEGnow5cHIDDJ3XK5fTdJ24cd8HwPC78btPE5Y4i6bybYT7a6nKe5X4Ud+ksfwzvI15uIbcGlK91VXHWWt1kWXPIL2iAt/z/4ll+BjsV11PYnp2sJBh0LR1Bc6EcZeubADRlFK9O3KnA3V1dURGRlJbW4vL5errcIQQQvRXR96HEUt7bexHd/N5G3lnzY8xR84nPDyapEIPVXl5BKpOk+ipIXXUUMKX3Y92ah0MmRPcz6Yf68z9WzaWE0IIMTClTITiEHZk64eaT59m+xsrqW2aw/XTl5ESncJntlKir53DvJ/8nLivPsRxI5rNjz6M4cro94lHZ0nPhxBCiIHr0NvB3o8BtuhY2eb12NKGE3A5eWfVOr76xZsB2JqzlbySPGaMmoHu1tF1ndTU1D6ONjQ91vPxxBNPMHXqVCIiIkhISGDZsmUcPXq0VZl7770XTdNafc2YMaPzrRBCCCE6MnwJHHwTWvZM6f9K9xyi9nQdDpeNmKhIJowdwQeb1qOUYkbWDMZkj2H3kd2Eh4cPmMSjszqVfKxfv54HH3yQrVu3smrVKvx+P4sXL6axsbFVuWuvvZaSkpKWr/fff79bgxZCCCGA4M62I2+Ag29AXXFfRxOSstPFHIjMZm+ej9VbCjlebKW0LIZHn/6InIoSqtxVLJuzjOjogblvSyg6Ndvlww8/bPXzc889R0JCAjt37mTu3HOb/NhsNpKSki48XQghhOh+VieMvQ0Kd0DRrmBvSD98DOP3uXnpo7cY5HCxcHIKZbVeMga5iIywYrWYUGoMj324ga9eNaSvQ+1xlzXVtra2FoCYmJhWx9etW0dCQgJRUVHMmzePxx9/nISEthdG8Xg8eDznVnKrq6u7nJCEEEJ8XqVNAb8XDr8NQxcE1wPpLzz1vLL6B5hSvkCpyUZkpIPI8xYQK6hq4lBJHfdMmcyQyPA+DLR3dHnAqVKKm2++merqajZu3NhyfMWKFYSHh5Oens7Jkyf56U9/it/vZ+fOndhsFy8J++ijj/LYY49ddFwGnAohxOdbwAhQ0VxBSWMJPsNHtC2aeGc8LquLovpSjpfqGEpDAzRNo7gyn8XDGzDpYbhKS9BTJ4MrpU/b4NvwPBZ/EZit7AmPBoud9fVhXDNsKtmJaS3lVh06zTXZA3eXWujcgNMuJx8PPvgg7733Hps2bSItLa3dciUlJaSnp/PKK69w660XL7rSVs/HoEGDJPkQQojPERUIsKV0K34VQNeCwxF1dOKccSSHJWPRLVS7qylvLqfeW8/a44f47vS7CbfZW+p4Z8cabpyyEJ+vjg9eXEFkRTzZc+OJmzQVzWLt1fY0r1+JPy8HPcKF8vkIv305AEVFn7H/xMecsmSTHj2EJVkTWX34NGNSI0mJcnRQa//WmeSjS49dHnroId5++202bNhwycQDIDk5mfT0dHJyctp83WaztdkjIoQQ4nOgqQoKtuEP+KiqOcQNV/2o3aLJ4ckkhwcXLN9TUNUq8TifxeLihq/ex9/+60kyjXHkr98EhtGyFpk1Iozo4VnYYuK6vTm+Y3txb34f66hJRNzzQwCaV7+K/+QhzEOySU2dTmJcNhvef5DP/FNQvhjmZaURZvt8LTjeqdYqpXjooYd48803WbduHUOGdDwoprKykoKCApKTkzssK4QQ4nOi/jQUbgdHNGQtxqLrhG05ijIMNL2r61+e68jXdRMLb5vNZ29sYunXb8cRfS5R8VRVUH00B0/9HgAMn5+Uq6Zhi+7aGBFlGDR/9A8C5aVYMjKJ+OqPW626ap9/K42v/YnwIdkQ8GE++gELb/wrC8292xvTn3Qq+XjwwQd56aWXeOutt4iIiKC0tBSAyMhIHA4HDQ0NPProo3zhC18gOTmZU6dO8ZOf/IS4uDhuueWWHmmAEEKIAaS5Bk5tgvBEGHl9q5v0uKyb2HtoBRPGfLlbLpU+bAL5Y3N5+y8vMSV7NhmLh2GymrDFxJE081yvh/IHKNq4EV+Tm0HzZ2MOC33AZ1NxMU0r/kTUHfdjTmn7D3LNbAZvAxz7CJQB2TfD5zjxgE4mH08++SQA8+fPb3X8ueee495778VkMrF//35eeOEFampqSE5OZsGCBaxYsYKIiCtraVghhBChUUpRcHAfvqZ69P0rGHLvb8F08e0nNm44+3Le7rC+Bo8be4g37znX3kbV1AJWvfEeB/9vB2NGTGPwogzMjnPX18wm0hbMx/D6KFi3HlCkzZ+Lydr+kIDKA/uoyy+hcvMxxv37jzG72k9YGrdtwzR0EmQtHrD70HQ3WV5dCCFEj1BK0bxrF3vXfsyYu79GRGISNUe3U194jEGLvtLmOTnHP8JuDWfQoFnt1vv+0Z2kR8UzOnFwq+Pv7FjNjVOubve8k8d3sHXVDsaFz2DwgmFEpLb9R7G/qYnC9Rsx2a2kzZmLZjYBYPj9FG/ahK/JQ0xWBsqtUZdTwOBbF7XdfsOgfvVq7CNGYE1PbzeuK0WPDzgVQggh2uMrKsJ97BgAjgkTmDbu39jzpz8w8dsPETViKv6meko2v0XyVTdfdG7WsCV8suW/Lpl85NWUcm3WxDZeuXSvwpBhU6iozsNf6aF8Rxk2lw1rxMU9KGank4ylS/DWVJO/Zg3amd4KpRTJ06ZgjQquPGr4A1TtPtb2e1BSQvPevYTPmYMeFnbJuD6PJPkQQgjRLXwBHxuKNuB6cz1TvvNYy00bYMy997HvL08y/lsPETdxIaVb3qZ892riJ17cUzEsYQKHct4nO+u6Nq9jKIXexqBUn6/jRSojwqMJuBWRI8PJ/+gUQ5ZlYjK3PcDVGhVN+uJr2q1LN5tQ5+0powIBmj77DOXzYYqKwnXttR3G83nV1SHFQgghBFU1FazMXcnGwo1sL93O3NS5mEeNoLL0ZKtytsgoht1wM4eefwaApJk34akup/rw1ovqzBh2DRWVh2loKm/zmj7D2+ZxiyWyw3iHDp2OL7yMwqN5pC/NIO+dEx2ecynmMCfNBSU0bNhAw/oN2MeNJ3zePBzjx19WvVc6ST6EEEJ0mq+sieZDlZS/dpgJ+mjmpM3hqtSrsJgsjL/6yxxdu/KicyIGp5MweSq5/3wFgLSFX6a++ARlOz68qOycqf/Krn0vXnT8j1vfZV76pDZjMlTHO9tarWEYhh+bw46BQeLMZHJXHMXX5Ovw3IuuZyi8g0bSePAIYTNmELFwAaZwecQSCkk+hBBCXJIyFL7yJtzHqmk+WkXzoUrQwJEdS/qCMVRvzuf8uQu6rhM7ZCR5R7ZfVFf8uPE4UlI59f67AAxedCea2capD56B8+rQTGb8vkYunBOhaTAxNaPNOK1mC02e5hBapBGbnkB1QQVhSWFk3DSMkk1FIZwHNaebqCxuIP9gJSf3lDNkcjJx1y5As36+p852liQfQgjxOaAMhbewnuajVbiPVuE+Vt3qv77TjSjj3I3eX+Om+XAlzUeq8OTWoOkatswoHCNicGTHYol3AmAbGkVsWCyH9+xudb2RM6+jcNdG2pI6aw7oOkUbNwAQP2EBCRMWcvL9v7QqN3n0l3lv869aJSDGJSZoThk6hp3H91zyfSgsegm//yhRybHUnK4EwOwwYwQUhr/tnpOGajcn95Zzan8FSimsdjNpo2IYNikBs8V0yeuJtknyIYQQV7BAg5fmQ5W4j1VjctlwjIjBPiIG+/Dolv/ahkejWUx4cqppPlJF88FKjEY/jlGxOEYGy+SWO/jafRfPJtE0jcjwaOrqavD7Wj+6GDxpDgc/bXvdjoxrr6OprJTyPbsAcCYPwQi0vvlHxgxjduYNvPLRQwC4fV6qmmvbbavFbCdg+C/5fvi8VYSHZ1JdVcBBpXjiRAkBpYibksjpraWtyiqlKDhcRV2FmyHj48kYG0d0UhgRMXZ0XdbruBySfAghxBXKc6oW3+kmHNnBJMLkavvRgKZpmGPs2EfE4BgZg2N0LNbU0Ff5NEVYyc4cz77PPmt1vJGpfOu72dx9d9vjKbK+cDsV+/dRezwXgOihYyjd3DpZiUocS0byFLwNp/n2O//LN6fe1G4cvoCB+YKOiKON7pbvDx95hLr6A+i6Fa/Hzyink2tiXehAeLwTT70HpRTFOdWc2ldB/sEq4gdHkJIVFfJ7IUIjyYcQQlyB3DnV6OFW7MOiulyH3w+33QZXXw1//GP75ZyTEuBYI7rDQmFRXqvXTOEZrPqgmXvvbfvcUXffS97qj2k8fZqYMbMxh0dTtH5FqzJTRt3ORzv+jynJQ/H5A+3Ha4BZa/1YZrjz3CqlQ4d8F6cjA5+vkeiYVBxGgCmRYWiahuEJ4DRp5O2tIDopjIxxcaSPicUeZmm/4aLLJPkQQogrjOdEDeZYB5a4y9uifeVKGD4cVq+GcePaL6dbTWAoJkyeTunJYq5d0kBUFIweDdu3mwkoC+vWedo9f8w3vsmxf76Cp76euHFzCASgqeBgy+sWq5PZ4+9jelgT/9i3ut16/IEAFlPr29r5a43YbPGYzeFn6rTg8/lxn3nU5C2oI/7qdDImxONoY+Ex0b0k+RBCiCuIr6IZzWrCHNP2dvOdkZsLkycHv582LbRzTpXOxO32YHf4UQqam8HtdeBzN7V7jq7rjP+Xb3Hwb8E1QAYtuJ3SvZ+2KlNRlYPNHoPT1v5+Kx6/D4u59dqZtbV7aGzMbXWssbGYgwefg+qTWNNdwXEtmdFoMo6j18gKp0IIcQXxlzbiGBPXccEQZGbC7t3whS/Ajh2XLmv4DcoOlHLffUnU18cACk0LTo1taoLYSBuHd3zMqCmL2zxft1pxRMeiAgE0k6lVj8W+w69jCfjJ0TK4emhquzF4/X4MXxkVFWvQNBNKBYiIGE0g0ExF5To0NNye02RmLsJkchA1YSqaJn+D9wVJPoQQ4grhLW7Akhb6QNGOLFsGr7wCixbByJGXLltRW8Cvnx2Fw+LBGmuhocHA79dxOsHjAbPdSfmR3WSOn4fF0nbvhTU6GndZGY7kZJRSKMNA03VqavOYO+P7JDU1sPLQFkbGp7U6TynF3sJaiqoDzEwfQUzkiIvqdjozAIiNndeVt0J0M0k+hBDiCuE5UYstw4W34eLZJZakMLR29jBpj9kMr73WcTl3bT2aw8ThQj+J8WXUVMdS6XEAiqYmDdOZGShJ42ZQtP8zMibNbbOeiOQU6gvycCQnEz1iKtX71hIzYRGaHhz0Ge0Mp87TxIGiWsobgmNINEABo5JcTBgU1an2ib4jyYcQQlwhwqYmoXwXzwbx5NZgSe65Zb8rivMwfAZpKUfJPTKSUYMaKa+14/Zo6Dqc3QNu+Lh5bHrxv9pMPpRSOJxhlB/NJwGIzprIybf+QEyYQa2vgU1FmwAYkZBIgsvGmNSO93ER/ZckH0IIcYXQbSawXbzipu4wo5l6bmxDysjR/GXNH7kl6QBrjP9i/b4wTGYwmSAyEmJjYcaMYNnwpDSqTucRk5jeqo76j1dhTk1BT07i5J6doBQRqUNh6AJuyGp/Z1kxMEnyIYQQVzg9woq/shlzbPtTb5UvgNbGUuE1NTVERka2GgB6Uf2axr3ZKVi//SBLJ/+K1z5byAH7aL7+wHE2fzqejRvh+eeDZcctuJ2tb/yJq774UKs6zHGx2AYPJsPl6lIbxcAiw3yFEOIKZ00Jx1fejLew/qKN2pRSeE7V4j5ajTu3+qJzDaPjnWIBdFc0RmUl4ff8iEXWD8jJNfO1e4eya9cF5UwmNCAQuGAZdF1HBdpfQExcWaTnQwghPgccI2Pw13pwH6s+14uhFAqwpoZjyojEk1eHt6ih1dLqMTExIdWvDx5HYPsbmMZNIjwrnud/Vk9ZSTEjx4y/qOyoBbeyd/UrTFpyFwDe/HyM+nrM0dGX3U4xMEjyIYQQnxPmSBvmyPYX6bKlu/BXNNN8pApN11B+A0tyGObojhcsM8XH4k2aCzTiHD0St0knYLTdkxEVl4qnqJCG9etB0zDFxBI+t+0ZMOLKJMmHEEKIFuY4B+Yzy7K7c2swhXdiqXF7HKrqABCFSTejjHOPeAxlcKr2FIUNhQBELV1CePLE7gxdDCCSfAghhGibBspQhLLouDunGktiGIQtRQUqMU41caLhBNWFwWXVdU0n3ZXOnNQ5lxy8Kj4fJPkQQgjRJtuQSJr2luPIjg1O4wWU30AFFKAI1Hjw13ggoLAOisB0ZkM2xwQnDuAmvth3wYt+TZIPIYQQbdJ0Def4eDwnasFQoBToGpolOFHSFGXDniUbsonOk+RDCCFEuzRdw54Z1ddhiCuMrPMhhBBCiF4lyYcQQgghepUkH0IIIYToVZJ8CCGEEKJXSfIhhBBCiF4lyYcQQgghepUkH0IIIYToVZJ8CCGEEKJXSfIhhBBCiF4lyYcQQgghepUkH0IIIYToVZJ8CCGEEKJXSfIhhBBCiF4lyYcQQgghepW5rwO4kFIKgLq6uj6ORAghhBChOnvfPnsfv5R+l3zU19cDMGjQoD6ORAghhBCdVV9fT2Rk5CXLaCqUFKUXGYZBcXExSikGDx5MQUEBLperr8PqMXV1dQwaNOiKbydIW69U0tYrk7T1ytPT7VRKUV9fT0pKCrp+6VEd/a7nQ9d10tLSWrpvXC7XFf3LcNbnpZ0gbb1SSVuvTNLWK09PtrOjHo+zZMCpEEIIIXqVJB9CCCGE6FX9Nvmw2Wz8/Oc/x2az9XUoPerz0k6Qtl6ppK1XJmnrlac/tbPfDTgVQgghxJWt3/Z8CCGEEOLKJMmHEEIIIXqVJB9CCCGE6FWSfAghhBCiV/W75OPYsWPcfPPNxMXF4XK5mDVrFmvXrm1VZvv27SxatIioqCiio6NZvHgxe/bs6ZuAL0NHbX3++efRNK3Nr7Kysj6MvPNC+Vwh2OZx48Zht9tJSkpi+fLlfRDt5QmlrW19pk899VQfRdx1oX6uAJWVlaSlpaFpGjU1Nb0baDfoqK2VlZVce+21pKSkYLPZGDRoEMuXLx+Q+1R11Na9e/fy5S9/mUGDBuFwOBg1ahS///3v+zDirgnl9/df//VfmTx5MjabjQkTJvRNoN0glLbm5+dz4403EhYWRlxcHN/5znfwer09Ek+/Sz6uv/56/H4/a9asYefOnUyYMIEbbriB0tJSILhm/JIlSxg8eDCfffYZmzZtwuVysWTJEnw+Xx9H3zkdtfWOO+6gpKSk1deSJUuYN28eCQkJfRx953TUVoDf/OY3PPLII/z4xz/m4MGDfPLJJyxZsqQPo+6aUNoK8Nxzz7X6bL/61a/2UcRdF2pbAe6//37GjRvXB1F2j47aqus6N998M2+//TbHjh3j+eefZ/Xq1Xzzm9/s48g7r6O27ty5k/j4eF588UUOHjzII488wsMPP8wf/vCHPo68c0L5/VVKcd9993HHHXf0YaSXr6O2BgIBrr/+ehobG9m0aROvvPIKr7/+Ov/2b//WMwGpfqS8vFwBasOGDS3H6urqFKBWr16tlFJq+/btClD5+fktZfbt26cAlZub2+sxd1Uobb1QWVmZslgs6oUXXuitMLtFKG2tqqpSDoej3bYPFKF+roB68803+yDC7tOZ3+E//elPat68eeqTTz5RgKquru7laC9PV/5/VUqp3//+9yotLa03Quw2XW3rt7/9bbVgwYLeCLFbdLadP//5z9X48eN7McLuE0pb33//faXruioqKmop8/LLLyubzaZqa2u7PaZ+1fMRGxvLqFGjeOGFF2hsbMTv9/P000+TmJjI5MmTARgxYgRxcXE888wzeL1empubeeaZZxg9ejTp6el93ILQhdLWC73wwgs4nU5uu+22Xo728oTS1lWrVmEYBkVFRYwaNYq0tDRuv/12CgoK+jj6zunM57p8+XLi4uKYOnUqTz31FIZh9FHUXRNqWw8dOsR//Md/8MILL3S42VR/1ZX/X4uLi3njjTeYN29eL0d7ebrSVoDa2lpiYmJ6MdLL09V2DkShtHXLli2MGTOGlJSUlvOWLFmCx+Nh586d3R9Ut6czl6mwsFBNnjxZaZqmTCaTSklJUbt3725V5sCBA2rYsGFK13Wl67oaOXKkysvL65uAL0MobT1fdna2+ta3vtV7AXajjtr6xBNPKIvFokaMGKE+/PBDtWXLFrVo0SI1YsQI5fF4+i7wLgjlc/3FL36hNm/erHbv3q3++7//WzmdTvWLX/yibwK+DB211e12q3Hjxqm///3vSiml1q5dOyB7PpQK/f/XL33pS8rhcChA3Xjjjaq5ubn3g71Mnf23afPmzcpisaiPP/6494LsBp1p50Du+VCq47Z+4xvfUNdcc81F51mtVvXSSy91ezy9knz8/Oc/V8Alv7Zv364Mw1A33XSTWrp0qdq0aZPauXOn+ta3vqVSU1NVcXGxUkqppqYmNW3aNHXPPfeobdu2qS1btqgvfOELavTo0aqpqak3mnNJ3dnW823evFkBaseOHX3QqrZ1Z1sff/xxBaiPPvqopf6ysjKl67r68MMP+6qJLXrqcz3rv//7v5XL5erFFrWvO9v6ve99T91xxx0tdfe35KMnPteSkhJ1+PBhtXLlyn71B0NP/Q4fOHBAxcfH95vkuafa2R+Tj+5s6ze+8Q21ePHii65hsVjUyy+/3O2x98ry6hUVFVRUVFyyTEZGBp9++imLFy+murq61Xa/WVlZ3H///fz4xz/mmWee4Sc/+QklJSUtXbher5fo6GieeeYZvvSlL/VoWzrSnW093/3338+uXbvYvXt3j8TdFd3Z1ueee4777ruPgoIC0tLSWsokJibyy1/+km984xs91o5Q9NTnetann37K7NmzKS0tJTExsVtj76zubOuECRPYv38/mqYBwcF7hmFgMpl45JFHeOyxx3q0LR3p6c9106ZNzJkzh+LiYpKTk7s19s7qibYeOnSIBQsW8PWvf53HH3+8x2LvjJ76TB999FFWrlzZr2ZWdmdbf/azn/HWW2+xd+/elterq6uJiYlhzZo1LFiwoFtjN3drbe2Ii4sjLi6uw3JNTU0AFz0X1nW95Xl4U1MTuq63/GN29nVN0/rFM/PubOtZDQ0NvPrqqzzxxBPdF2g36M62zpo1C4CjR4+2JB9VVVVUVFT0i7E8PfG5nm/37t3Y7XaioqIuK87u0J1tff3112lubm55bfv27dx3331s3LiRYcOGdWPUXdPTn+vZv+08Hs9lRNk9urutBw8eZOHChXz1q1/tN4kH9Pxn2p90Z1tnzpzJ448/TklJSUui/PHHH2Oz2XpmDEy396VchvLychUbG6tuvfVWtWfPHnX06FH1gx/8QFksFrVnzx6llFKHDx9WNptNfetb31KHDh1SBw4cUHfddZeKjIy8ZLd2fxNKW8/661//qux2u6qqquqjaC9PqG29+eab1ejRo9Wnn36q9u/fr2644QaVnZ2tvF5vH0bfOaG09e2331Z//vOf1f79+1Vubq76y1/+olwul/rOd77Tx9F3Tmd+h8/qb49dQhVKW9977z317LPPqv3796uTJ0+q9957T40ePVrNmjWrj6PvnFDaevZRy1e+8hVVUlLS8lVWVtbH0Ycu1N/fnJwctXv3bvXAAw+o4cOHq927d6vdu3cPqLFoobTV7/erMWPGqEWLFqldu3ap1atXq7S0NLV8+fIeialfJR9KBafSLl68WMXExKiIiAg1Y8YM9f7777cq8/HHH6tZs2apyMhIFR0drRYuXKi2bNnSRxF3XShtVUqpmTNnqjvvvLMPIuw+obS1trZW3XfffSoqKkrFxMSoW265pdWU6oGio7Z+8MEHasKECSo8PFw5nU41ZswY9bvf/U75fL4+jLprQv0dPmugJh9KddzWNWvWqJkzZ6rIyEhlt9tVVlaW+tGPfnRFtrW9sQbp6el9F3QXhPL7O2/evDbbevLkyb4JuotCaWteXp66/vrrlcPhUDExMWr58uXK7Xb3SDy9MuZDCCGEEOKsgTnpXgghhBADliQfQgghhOhVknwIIYQQoldJ8iGEEEKIXiXJhxBCCCF6lSQfQgghhOhVknwIIYQQoldJ8iGEEEKIXiXJhxBCCCF6lSQfQgghhOhVknwIIYQQoldJ8iGEEEKIXvX/A/k6SmAjtgIVAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"sanity check - Dem-leaning vtds for year\",years[0])\n",
    "for v in range(nVTDs):\n",
    "    plotPoly(vtdGeom[v],0.2)\n",
    "    if DemVotes[0][vestID[v]] > RepVotes[0][vestID[v]]:\n",
    "        plt.text(vtdGeom[v].centroid.x, vtdGeom[v].centroid.y, \"d\", color = \"blue\",fontsize=6)\n",
    "        #plotCenter(\"d\",vtdGeom[v],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "56ed7e9d-1400-4848-892e-737555552d58",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\n",
      "working on drawn district no 0\n",
      "working on drawn district no 1000\n",
      "working on drawn district no 2000\n",
      "working on drawn district no 3000\n",
      "working on drawn district no 4000\n",
      "working on drawn district no 5000\n",
      "working on drawn district no 6000\n",
      "working on drawn district no 7000\n",
      "here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\n",
      "true 2020: 5296164 5667685 371520\n",
      "ensemble : 5284714 5653151 368437\n",
      "the true and ensemble state GOP leans are 0.51694 0.516842272600914\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\")\n",
    "nDrawnDistricts = len(HDweight)  #now including cut districts\n",
    "nMapDistricts = nCutDistricts + nDistricts\n",
    "nEnsembleDems, nEnsembleReps = [0.]*nYears, [0.]*nYears\n",
    "y=0\n",
    "for t in range(nDrawnDistricts):\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on drawn district no\",t)\n",
    "    nEnsembleDems[y] += nMapDistricts* HDweight[t] * ( np.sum([DemVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([DemVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "    nEnsembleReps[y] += nMapDistricts* HDweight[t] * ( np.sum([RepVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([RepVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "\n",
    "print(\"here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\")\n",
    "print(\"true 2020:\",int(vestDems[y]),int(vestReps[y]),              int(vestReps[y] - vestDems[y]) )\n",
    "print(\"ensemble :\",int(nEnsembleDems[y]),int(nEnsembleReps[y]),    int(nEnsembleReps[y] - nEnsembleDems[y]) )\n",
    "print(\"the true and ensemble state GOP leans are\",r5(vestReps[y]/(vestReps[y]+vestDems[y])),\n",
    "      nEnsembleReps[y]/(nEnsembleReps[y]+nEnsembleDems[y]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "5c607c63-df0d-4334-8131-368c090c76d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7213 7211 26\n"
     ]
    }
   ],
   "source": [
    "print(nDrawnDistricts,nHDs, nDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb0d0d12-6bf4-4d5f-9236-fa8735393e98",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
